{
  "data": {
    "a": {
      "slug": "gemma-4-e4b-it",
      "name": "Gemma 4 E4B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Gemma 4 E4B IT is Google DeepMind's small open-weight instruction-tuned model for phones and laptops, 4.5B effective parameters (8B with per-layer embeddings), taking text, images, video frames and audio with a 131,072-token context, published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gemma-4-e4b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e4b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e4b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json",
      "dns": "gemma-4-e4b-it.grade.anchorterminal.com",
      "repo": "https://huggingface.co/google/gemma-4-E4B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/gemma-4-E4B-it"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-E4B-it-qat-q4_0-gguf"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-E4B-it-qat-w4a16-ct"
        },
        {
          "registry": "ollama",
          "name": "gemma4:e4b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. No hosted version, since the Gemini API serves only the 26B A4B and 31B sizes and Hugging Face lists no inference provider for it. Running it yourself costs the hardware, 17.9 GB at BF16, 8.9 GB at SFP8, 4.5 GB at Q4_0, 2.5 GB for the mobile build and 2.2 GB for the mobile build text only to load the weights per Google (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://gemma.ai/models/gemma-4/",
      "llmsTxt": "https://gemma.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "on-device",
        "multimodal",
        "audio",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "qat",
        "self-hosted"
      ],
      "lastRelease": "2026-07-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 63.2,
        "grade": "B",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 18,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 27,
          "interface": 85,
          "reach": 95,
          "risk": 70,
          "upkeep": 75
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-02"
        },
        "negative": 0,
        "verdict": "A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 7 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.",
        "bestFor": "Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning",
        "strengths": [
          "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file",
          "Google's own 4-bit QAT GGUF needs about 7.4 GB with working space",
          "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers",
          "Tool declaration and call format documented by Google, with parallel calls and a shipped response parser",
          "131,072 tokens of native context and thinking that turns on or off per request",
          "Takes audio as well as images and video frames"
        ],
        "weaknesses": [
          "No safety evaluation figures published",
          "No documented way to ask for JSON or schema output",
          "No hosted version, since the Gemini API serves only the two largest sizes",
          "Training data described only by type and cut-off",
          "Google's own card numbers put it far below the 12B, 26B A4B and 31B on agent and coding tasks",
          "Chat-template reports since June wait without a reply"
        ],
        "agentNotes": [
          "On the reference machine run `llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E4B-it-mmproj.gguf for images and audio",
          "With Ollama run `ollama run gemma4:e4b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K",
          "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there",
          "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns",
          "Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 63.2
          }
        ],
        "editorialScores": {
          "capability": 27,
          "interface": 85,
          "reach": 95,
          "risk": 70,
          "upkeep": 75
        },
        "provenanceScore": 72,
        "checked": {
          "categories": {
            "capability": "2026-11-02",
            "interface": "2026-11-02",
            "reach": "2026-11-02",
            "risk": "2026-11-02",
            "upkeep": "2026-11-02"
          },
          "last": "2026-11-02",
          "oldest": "2026-11-02",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run gemma4:e4b\n# or, with llama.cpp and Google's QAT build:\nllama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gemma4:e4b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gemma-4-e4b-it"
      },
      "sameCompany": [
        "embeddinggemma-2",
        "embeddinggemma-300m",
        "shieldgemma",
        "diffusiongemma-26b-a4b-it",
        "gemma-4-12b-it",
        "gemma-4-26b-a4b-it",
        "gemma-4-31b-it",
        "gemma-4-e2b-it"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-E4B",
        "params": "8B",
        "context": 131072,
        "goodFor": "Small on-device assistant and agent model, 4.5B effective, with tool calling and optional thinking, taking text, images, video frames and short audio clips for transcription and translation.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gemma-4-E4B_q4_0-it",
            "url": "https://huggingface.co/google/gemma-4-E4B-it-qat-q4_0-gguf/blob/main/gemma-4-E4B_q4_0-it.gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 6.15,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Quantisation-aware trained by Google. Size is the repository's two files summed, the 5.15 GB model and the 0.99 GB projector (gemma-4-E4B-it-mmproj.gguf) for image and audio input"
          },
          {
            "name": "gemma-4-E4B-it-qat-mobile-ct",
            "url": "https://huggingface.co/google/gemma-4-E4B-it-qat-mobile-ct",
            "who": "publisher",
            "precision": "wNa8o8",
            "sizeGB": 3.77,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "Google's mobile scheme with 2-bit decoding layers, so below 4-bit in places and not the build Reach's memory line reads. Compressed-tensors format"
          },
          {
            "name": "gemma-4-E4B-it-qat-mobile-transformers",
            "url": "https://huggingface.co/google/gemma-4-E4B-it-qat-mobile-transformers",
            "who": "publisher",
            "precision": "wNa8o8",
            "sizeGB": 3.56,
            "memoryGB": 8,
            "format": "safetensors",
            "note": "The same mobile scheme as a Transformers reference, which Google says other formats are built from. Google's mobile memory figures are for LiteRT-LM"
          },
          {
            "name": "ollama gemma4:e4b",
            "url": "https://ollama.com/library/gemma4:e4b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 6.6,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M weights (5.5 GB) with a BF16 vision projector (992 MB) and a Q8_0 draft model (99 MB) (digest dc35e8d9c606), the llama.cpp variant. gemma4:e4b-it-qat (6.1 GB) is Ollama's copy of Google's Q4_0 QAT build"
          },
          {
            "name": "lmstudio google/gemma-4-e4b",
            "url": "https://lmstudio.ai/models/google/gemma-4-e4b",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 5.9,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "LM Studio"
            ],
            "note": "LM Studio's catalogue build, from lmstudio-community/gemma-4-E4B-it-GGUF, with MLX builds at 4 to 8 bits beside it. Size as the catalogue shows it, and Q4_K_M is inferred from that repository's files rather than stated by LM Studio"
          },
          {
            "name": "gemma-4-E4B-it-qat-w4a16-ct",
            "url": "https://huggingface.co/google/gemma-4-E4B-it-qat-w4a16-ct",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 11.55,
            "memoryGB": 16,
            "format": "safetensors",
            "note": "Quantisation-aware trained, 4-bit weights with 16-bit activations in compressed-tensors format, which Google routes to vLLM and SGLang. The per-layer embedding tables stay large, so it's bigger than the GGUF"
          },
          {
            "name": "gemma-4-E4B-it",
            "url": "https://huggingface.co/google/gemma-4-E4B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 16.02,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072",
        "runBuild": "gemma-4-E4B_q4_0-it",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Google (Gemma)",
        "family": "Gemma 4",
        "url": "https://www.anchorterminal.com/open-weights/#google",
        "others": [
          {
            "name": "gemma-4-26B-A4B",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B",
            "params": "25.2B total, 3.8B active"
          },
          {
            "name": "gemma-4-26B-A4B-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it"
          },
          {
            "name": "gemma-4-31B",
            "url": "https://huggingface.co/google/gemma-4-31B",
            "params": "30.7B"
          },
          {
            "name": "gemma-4-31B-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it",
            "params": "30.7B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it"
          },
          {
            "name": "gemma-4-E2B",
            "url": "https://huggingface.co/google/gemma-4-E2B",
            "params": "2.3B effective (5.1B with embeddings)"
          },
          {
            "name": "gemma-4-E2B-it",
            "url": "https://huggingface.co/google/gemma-4-E2B-it",
            "params": "2.3B effective (5.1B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it"
          },
          {
            "name": "gemma-4-E4B",
            "url": "https://huggingface.co/google/gemma-4-E4B",
            "params": "4.5B effective (8B with embeddings)"
          },
          {
            "name": "gemma-4-26B-A4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant",
            "params": "420M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-31B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-31B-it-assistant",
            "params": "470M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E2B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant",
            "params": "78M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant",
            "params": "79M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-12B",
            "url": "https://huggingface.co/google/gemma-4-12B",
            "params": "11.95B"
          },
          {
            "name": "gemma-4-12B-it",
            "url": "https://huggingface.co/google/gemma-4-12B-it",
            "params": "11.95B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it"
          },
          {
            "name": "gemma-4-12B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-12B-it-assistant",
            "params": "423M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "diffusiongemma-26B-A4B-it",
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it"
          },
          {
            "name": "embeddinggemma-2",
            "url": "https://huggingface.co/google/embeddinggemma-2",
            "params": "740M",
            "listing": "https://www.anchorterminal.com/tools/embeddinggemma-2"
          }
        ],
        "olderCount": 171,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Google LLC (RDAP registrant of gemma.ai as recorded for the first batch's Gemma 4 listings; the card names Google DeepMind as author)",
        "domain": "gemma.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://gemma.ai/models/releases/",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The registrant and registration date are carried from the gemma-4-26b-a4b-it and gemma-4-31b-it facts read earlier on 21 October 2026, since rdap.org rate-limited our request this time and wasn't retried.",
          "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "gemma.ai keeps a dated releases page for the Gemma family (this size on 31 March 2026). The Hugging Face commit history says what each change fixed.",
          "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme."
        ],
        "score": 72
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-e4b-it.json",
      "live": {
        "slug": "gemma-4-e4b-it",
        "securityTxt": {
          "url": "https://gemma.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:36.94110613Z"
        },
        "llmsTxt": {
          "url": "https://gemma.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-11-10T15:43:16.745318902Z"
        },
        "domain": {
          "domain": "gemma.ai",
          "registered": "2017-12-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/gemma.ai",
          "checkedAt": "2026-11-08T15:00:32.790630815Z"
        },
        "updatedAt": "2026-11-10T16:08:36.94110613Z"
      }
    },
    "answer": "Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in every scored category.",
    "b": {
      "slug": "hy-memory-30b-a3b",
      "name": "Hy-Memory-30B-A3B",
      "vendor": "Tencent (Hy team)",
      "vendorUrl": "https://aistudio.tencent.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Hy-Memory-30B-A3B is Tencent Hunyuan's open-weight memory writer, a 30B mixture of experts with about 3B active, trained to decide what an agent's memory framework keeps and updates inside Mem0, MemOS and Hy-Memory, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json",
      "dns": "hy-memory-30b-a3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/tencent/Hy-Memory-30B-A3B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "tencent/Hy-Memory-30B-A3B"
        },
        {
          "registry": "huggingface",
          "name": "tencent/Hy-Memory-30B-A3B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "tencent/Hy-Memory-30B-A3B-FP8"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main, FP8 and GGUF repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 60.1 GB for the BF16 files, 30.6 GB in FP8 or 18.2 GB for Tencent's Q4_K_M GGUF (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/tencent/Hy-Memory-30B-A3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "agent-memory",
        "specialist",
        "apache-2.0",
        "safetensors",
        "fp8",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-10-16",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 54,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 65,
          "reach": 85,
          "risk": 60,
          "upkeep": 68
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them.",
        "bestFor": "Agents with an external memory framework that want a local model for the write path on a 24 GB card",
        "strengths": [
          "Made for one job, deciding what a memory framework stores and updates, and tested in four frameworks",
          "Fits a 24 GB card from Tencent's own Q4_K_M GGUF",
          "Runs in llama.cpp, vLLM, SGLang and Transformers from their releases",
          "Plain Apache 2.0, ungated, safetensors and GGUF only",
          "Tencent replied to three of the four discussions"
        ],
        "weaknesses": [
          "Not a general assistant or a tool caller",
          "No limitations, safety results or guidance on untrusted text in what it reads",
          "Training data isn't described, and the card gives no context length",
          "The results are on a 20% subsample, run by Tencent",
          "No inference provider, so it has to be run locally"
        ],
        "agentNotes": [
          "Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user",
          "Start it with `llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja`, or with vLLM under the served name hy-memory as the card does",
          "Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does",
          "Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented",
          "Treat what it stores as unverified. It keeps what a conversation says, true or not"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 65,
          "reach": 85,
          "risk": 60,
          "upkeep": 68
        },
        "provenanceScore": 75,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja\n# or, with vLLM, as the card names it:\nvllm serve tencent/Hy-Memory-30B-A3B --served-model-name hy-memory",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"hy-memory\", \"temperature\": 0, \"messages\": [{\"role\": \"system\", \"content\": \"Extract explicit user facts and preferences. Return only a JSON object with a memories list of strings.\"}, {\"role\": \"user\", \"content\": \"I live in Seattle and I am vegetarian.\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"no_think\"}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/hy-memory-30b-a3b"
      },
      "sameCompany": [
        "hy-memory",
        "tencentdb-agent-memory",
        "hunyuanocr",
        "hy3",
        "wevisone-vl-2b",
        "wevisone-vl-4b"
      ],
      "weights": {
        "trainedBy": "Tencent (Hunyuan)",
        "builtOn": "Not declared. The architecture is Tencent's hy_v3, as Hy3",
        "params": "30.1B",
        "active": "3B",
        "context": 262144,
        "goodFor": "The write path of an agent's memory framework. It reads a conversation and decides what to store and how to update it, in Mem0, MemOS or Hy-Memory, in place of a general model.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Hy-Memory-30B-A3B-Q4_K_M",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 18.24,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "The smallest file at 4 bits. Q3_K_M (14.45 GB) and the two files with experts at 1 or 2 bits are below the 4-bit line and are not listed"
          },
          {
            "name": "Hy-Memory-30B-A3B-Q6_K",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 24.71,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Hy-Memory-30B-A3B-FP8",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 30.58,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "Hy-Memory-30B-A3B-Q8_0",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 31.99,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Hy-Memory-30B-A3B",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 60.13,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          }
        ],
        "run": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja",
        "runBuild": "Hy-Memory-30B-A3B-Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Tencent (Hunyuan / Hy)",
        "family": "Hy-Memory",
        "url": "https://www.anchorterminal.com/open-weights/#tencent",
        "olderCount": 22
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Tencent (named in the LICENSE file's copyright line)",
        "domain": "tencent.com",
        "domainRegistered": "1998-09-14",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/tencent/Hy-Memory-30B-A3B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The LICENSE file reads Copyright (C) 2026 Tencent above the Apache 2.0 text, as on Hy3.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, five commits on 15 and 16 October 2026, the uploads, the card and the licence.",
          "The Hy3 listing found no security.txt on Tencent's domains and records Tencent's Security Response Center as the reporting route. unchecked on 10 November."
        ],
        "score": 75
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.json"
    },
    "edges": [
      {
        "by": 20,
        "category": "interface",
        "leads": "gemma-4-e4b-it",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, and ships a response schema for Transformers' parser. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 131,072 tokens native, which meets the 128K line.",
          "b": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65."
        },
        "scores": {
          "a": 85,
          "b": 65
        }
      },
      {
        "by": 10,
        "category": "reach",
        "leads": "gemma-4-e4b-it",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, Google's own 4-bit QAT GGUF with its projector is 6.15 GB, which with a fifth on top is 7.4 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build and two mobile builds, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one.",
          "b": "Memory 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB. Its Q3_K_M file and the two files with experts at 1 or 2 bits are below 4-bit precision and don't count. Runtimes 20, hy_v3 model code in released vLLM (HYV3ForCausalLM in the v0.33.0 registry), SGLang v0.5.23 (hunyuan_v3.py), Transformers v5.22.2 and llama.cpp b12242, 5 each. Builds 15, Tencent publishes an FP8 repository and a GGUF repository with Q4_K_M, Q6_K and Q8_0 files in its own organisation, so quantised builds at two precisions or more. Terms 15, Apache 2.0, with only a preamble naming Tencent as copyright holder above the licence text. Download 10, gated is false for the main, FP8 and GGUF repositories on 10 November 2026, so the files download with no account or terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and the card names no API. 25 + 20 + 15 + 15 + 10 + 0 = 85."
        },
        "scores": {
          "a": 95,
          "b": 85
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "gemma-4-e4b-it",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were found.",
          "b": "File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60."
        },
        "scores": {
          "a": 70,
          "b": 60
        }
      },
      {
        "by": 7,
        "category": "upkeep",
        "leads": "gemma-4-e4b-it",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google's JAX library fixed the Gemma 4 audio embedding that this size runs on 5 October 2026, 28 days before this check, while the Hugging Face files last changed on 20 July. The line drops to 15 after 4 November unless the files or reference code change. Responsiveness 10, Google replied to 12 of 26 questions and reports, the last on 2026-07-17, with the five opened from 21 September to 1 November unanswered. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but reports about them wait without a reply. 20 + 10 + 20 + 10 + 10 + 5 = 75.",
          "b": "Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata) are there. Training data 5, A general description of the two training stages inside the harnesses, with no datasets, sizes or cut-off. Last change 20, the last commit, on 16 October 2026, added the licence and the card, 25 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (16 to 30 October 2026), 3 have a reply from a tencent organisation member. Version policy 5, A first release, with nothing replaced. File health 10, Config, tokenizer and template agree on the architecture, the context and the thinking levels. 8 + 5 + 20 + 20 + 5 + 10 = 68."
        },
        "scores": {
          "a": 75,
          "b": 68
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Google DeepMind",
        "b": "Tencent (Hy team)",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-20",
        "b": "2026-10-16",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "3.5/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in every scored category.",
        "question": "Which is better for AI agents, Gemma 4 E4B IT or Hy-Memory-30B-A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 95 against 85",
          "Risk, 70 against 60",
          "Agent interface, 85 against 65",
          "Documentation and upkeep, 75 against 68"
        ],
        "also": null,
        "goodFor": "Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning",
        "slug": "gemma-4-e4b-it",
        "watchFor": "No safety evaluation figures published"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Agents with an external memory framework that want a local model for the write path on a 24 GB card",
        "slug": "hy-memory-30b-a3b",
        "watchFor": "Not a general assistant or a tool caller"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b.json",
        "title": "Agents-A1-4B vs Hy-Memory-30B-A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it.json",
        "title": "Apertus v1.5 8B vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b.json",
        "title": "Apertus v1.5 8B vs Hy-Memory-30B-A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e4b-it.json",
        "title": "Apodex-1.1-mini vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e4b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.json",
        "title": "Apodex-1.1-mini vs Hy-Memory-30B-A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it.json",
        "title": "Arete-27B vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b.json",
        "title": "Arete-27B vs Hy-Memory-30B-A3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e4b-it.json",
        "title": "BigBang-v1 vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e4b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-hy-memory-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-hy-memory-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e4b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-hy-memory-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-hy-memory-30b-a3b"
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      {
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-gemma-4-e4b-it"
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-hy-memory-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e4b-it"
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-hy-memory-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-v-4-7-1b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-v-4-7-35b-a3b.json",
        "title": "Hy-Memory-30B-A3B vs MiniCPM-V 4.7 35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-v-4-7-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Hy-Memory-30B-A3B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-super-120b-a12b.json",
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-super-120b-a12b"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-ultra-550b-a55b.json",
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-ultra-550b-a55b"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-north-mini-code-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-olmo-3-7b-think.json",
        "title": "Hy-Memory-30B-A3B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-olmo-3-7b-think"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b.json",
        "title": "Hy-Memory-30B-A3B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-35b-a3b.json",
        "title": "Hy-Memory-30B-A3B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-397b.json",
        "title": "Hy-Memory-30B-A3B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-9b.json",
        "title": "Hy-Memory-30B-A3B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it.json",
        "title": "Hy-Memory-30B-A3B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-4b-instruct-2507.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-0-8b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-122b-a10b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-2b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-397b-a17b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-4b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-9b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-27b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-35b-a3b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-27b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-next.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-next"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-omni-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-2b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-2b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-32b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-8b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Hy-Memory-30B-A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-recursive-flint.json",
        "title": "Hy-Memory-30B-A3B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ripple-65b-preview.json",
        "title": "Hy-Memory-30B-A3B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview.json",
        "title": "Hy-Memory-30B-A3B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-smollm3-3b.json",
        "title": "Hy-Memory-30B-A3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-smollm3-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-spark-x2-6-4b.json",
        "title": "Hy-Memory-30B-A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash.json",
        "title": "Hy-Memory-30B-A3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Hy-Memory-30B-A3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-unlimited-ocr-2.json",
        "title": "Hy-Memory-30B-A3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b.json",
        "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b.json",
        "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b.json",
        "title": "Hy-Memory-30B-A3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 10,
        "edge": "gemma-4-e4b-it",
        "gemma-4-e4b-it": 95,
        "hy-memory-30b-a3b": 85,
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "edge": "gemma-4-e4b-it",
        "gemma-4-e4b-it": 70,
        "hy-memory-30b-a3b": 60,
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 20,
        "edge": "gemma-4-e4b-it",
        "gemma-4-e4b-it": 85,
        "hy-memory-30b-a3b": 65,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 7,
        "edge": "gemma-4-e4b-it",
        "gemma-4-e4b-it": 75,
        "hy-memory-30b-a3b": 68,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "gemma-4-e4b-it": "A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 7 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.",
      "hy-memory-30b-a3b": "A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them."
    }
  },
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  "markdown": "Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Gemma 4 E4B IT: grade B, 63.2/100, rank #24 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-e4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json\n- Hy-Memory-30B-A3B: grade C, 61/100, rank #68 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Gemma 4 E4B IT (B)\n\nGood for: Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning\n\nAhead on:\n- Reach, 95 against 85\n- Risk, 70 against 60\n- Agent interface, 85 against 65\n- Documentation and upkeep, 75 against 68\n\nWatch for: No safety evaluation figures published\n\n### Hy-Memory-30B-A3B (C)\n\nGood for: Agents with an external memory framework that want a local model for the write path on a 24 GB card\n\nWatch for: Not a general assistant or a tool caller\n\n\n## Why the edge\n\n### Agent interface: Gemma 4 E4B IT +20\n\n- Gemma 4 E4B IT (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, and ships a response schema for Transformers' parser. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 131,072 tokens native, which meets the 128K line.\n- Hy-Memory-30B-A3B (65): Chat template 20, chat_template.jinja in the repository. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65.\n\n### Reach: Gemma 4 E4B IT +10\n\n- Gemma 4 E4B IT (95): Memory 30, Google's own 4-bit QAT GGUF with its projector is 6.15 GB, which with a fifth on top is 7.4 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build and two mobile builds, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one.\n- Hy-Memory-30B-A3B (85): Memory 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB. Its Q3_K_M file and the two files with experts at 1 or 2 bits are below 4-bit precision and don't count. Runtimes 20, hy_v3 model code in released vLLM (HYV3ForCausalLM in the v0.33.0 registry), SGLang v0.5.23 (hunyuan_v3.py), Transformers v5.22.2 and llama.cpp b12242, 5 each. Builds 15, Tencent publishes an FP8 repository and a GGUF repository with Q4_K_M, Q6_K and Q8_0 files in its own organisation, so quantised builds at two precisions or more. Terms 15, Apache 2.0, with only a preamble naming Tencent as copyright holder above the licence text. Download 10, gated is false for the main, FP8 and GGUF repositories on 10 November 2026, so the files download with no account or terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and the card names no API. 25 + 20 + 15 + 15 + 10 + 0 = 85.\n\n### Risk: Gemma 4 E4B IT +10\n\n- Gemma 4 E4B IT (70): File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were found.\n- Hy-Memory-30B-A3B (60): File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n\n### Documentation and upkeep: Gemma 4 E4B IT +7\n\n- Gemma 4 E4B IT (75): Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google's JAX library fixed the Gemma 4 audio embedding that this size runs on 5 October 2026, 28 days before this check, while the Hugging Face files last changed on 20 July. The line drops to 15 after 4 November unless the files or reference code change. Responsiveness 10, Google replied to 12 of 26 questions and reports, the last on 2026-07-17, with the five opened from 21 September to 1 November unanswered. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but reports about them wait without a reply. 20 + 10 + 20 + 10 + 10 + 5 = 75.\n- Hy-Memory-30B-A3B (68): Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata) are there. Training data 5, A general description of the two training stages inside the harnesses, with no datasets, sizes or cut-off. Last change 20, the last commit, on 16 October 2026, added the licence and the card, 25 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (16 to 30 October 2026), 3 have a reply from a tencent organisation member. Version policy 5, A first release, with nothing replaced. File health 10, Config, tokenizer and template agree on the architecture, the context and the thinking levels. 8 + 5 + 20 + 20 + 5 + 10 = 68.\n\n## Score by category\n\n| Category | Weight | Gemma 4 E4B IT | Hy-Memory-30B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 85 | Gemma 4 E4B IT +10 |\n| Risk | 20% (20 this run) | 70 | 60 | Gemma 4 E4B IT +10 |\n| Agent interface | 10% (10 this run) | 85 | 65 | Gemma 4 E4B IT +20 |\n| Documentation and upkeep | 10% (10 this run) | 75 | 68 | Gemma 4 E4B IT +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63.2 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 E4B IT | Hy-Memory-30B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Tencent (Hy team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-20 | 2026-10-16 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.5/5 (4) | none |\n\n## Verdicts\n\n**Gemma 4 E4B IT.** A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 7 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.\n\n**Hy-Memory-30B-A3B.** A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them.\n\n## Before you call either\n\n### Gemma 4 E4B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E4B-it-mmproj.gguf for images and audio\n2. With Ollama run `ollama run gemma4:e4b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them\n\n### Hy-Memory-30B-A3B\n\n1. Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user\n2. Start it with `llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja`, or with vLLM under the served name hy-memory as the card does\n3. Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does\n4. Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented\n5. Treat what it stores as unverified. It keeps what a conversation says, true or not\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 E4B IT or Hy-Memory-30B-A3B?\n\nGemma 4 E4B IT scores 63.2 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy-memory-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy-memory-30b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-e4b-it\", \"b\": \"hy-memory-30b-a3b\"}`. From a terminal: `anchor compare gemma-4-e4b-it hy-memory-30b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json and https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json\n\n## Other comparisons with Gemma 4 E4B IT or Hy-Memory-30B-A3B\n\n- [Agents-A1-4B vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it.md)\n- [Agents-A1-4B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b.md)\n- [Apertus v1.5 8B vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it.md)\n- [Apertus v1.5 8B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b.md)\n- [Apodex-1.1-mini vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e4b-it.md)\n- [Apodex-1.1-mini vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.md)\n- 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    "title": "Gemma 4 E4B IT vs Hy-Memory-30B-A3B for AI agents (2026)",
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