# Hy-Memory-30B-A3B vs Nemotron 3 Ultra 550B A55B > Nemotron 3 Ultra 550B A55B scores 69.3 (B) to Hy-Memory-30B-A3B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-ultra-550b-a55b - Markdown: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-ultra-550b-a55b.md (~10,150 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 Nemotron 3 Ultra 550B A55B scores 69.3 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 3 of 4 scored categories. Hy-Memory-30B-A3B leads on reach. Both do open-weight models you run yourself. - 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 - Nemotron 3 Ultra 550B A55B: grade B, 69.3/100, rank #9 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Hy-Memory-30B-A3B (C) Good for: Agents with an external memory framework that want a local model for the write path on a 24 GB card Ahead on: - Reach, 85 against 55 Watch for: Not a general assistant or a tool caller ### Nemotron 3 Ultra 550B A55B (B) Good for: Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs Ahead on: - Risk, 70 against 60 - Agent interface, 90 against 65 Watch for: The smallest build is 352 GB and needs at least four B200-class GPUs ## Why the edge ### Reach: Hy-Memory-30B-A3B +30 - 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. - Nemotron 3 Ultra 550B A55B (55): Memory earns 0, because the smallest 4-bit build, NVIDIA's 352 GB NVFP4, needs about 423 GB with the fifth added, above the 256 GB band, and no runtime library has a local build. Runtimes earn 20 from released vLLM, SGLang, llama.cpp and Transformers, with Ollama's cloud-only entry not counted. Builds earn 10 for the single NVFP4 build. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it and two providers host it. ### Agent interface: Nemotron 3 Ultra 550B A55B +25 - 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. - Nemotron 3 Ultra 550B A55B (90): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and shows a tool call declared and read back. Trained tool calling earns 20, with tool-use training and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off or medium effort per request with a runtime parser that returns the reasoning apart. Context earns 10 for up to 1M tokens. ### Risk: Nemotron 3 Ultra 550B A55B +10 - 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. - Nemotron 3 Ultra 550B A55B (70): File safety earns 15, safetensors only but with 10 off because the documented commands pass trust-remote-code and the repository ships Python files. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA describes safety training but publishes no results. Agent guidance earns 15, as the technical report documents training the model to ignore instructions injected through tool output, beside the subcard's warnings and safeguards. Licence risk keeps all 15 under OpenMDW-1.1. The security programme earns 10 for the reporting route on the card and NVIDIA's public bulletins. ## Score by category | Category | Weight | Hy-Memory-30B-A3B | Nemotron 3 Ultra 550B A55B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 85 | 55 | Hy-Memory-30B-A3B +30 | | Risk | 20% (20 this run) | 60 | 70 | Nemotron 3 Ultra 550B A55B +10 | | Agent interface | 10% (10 this run) | 65 | 90 | Nemotron 3 Ultra 550B A55B +25 | | Documentation and upkeep | 10% (10 this run) | 68 | 70 | Nemotron 3 Ultra 550B A55B +2 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **69.3 · B** | | ## Facts side by side | Fact | Hy-Memory-30B-A3B | Nemotron 3 Ultra 550B A55B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Tencent (Hy team) | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | OpenMDW-1.1 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-16 | 2026-06-04 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2.8/5 (4) | ## How the scores have moved ### Hy-Memory-30B-A3B No score change since it was listed. ### Nemotron 3 Ultra 550B A55B - +0.5 on 1 November 2026: correction ## Verdicts **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. **Nemotron 3 Ultra 550B A55B.** NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results. ## Before you call either ### Hy-Memory-30B-A3B 1. Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user 2. 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 3. Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does 4. Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented 5. Treat what it stores as unverified. It keeps what a conversation says, true or not ### Nemotron 3 Ultra 550B A55B 1. It doesn't fit the 128 GB reference machine at any precision. Call NVIDIA's API (model nvidia/nemotron-3-ultra-550b-a55b) or a provider instead 2. With vLLM serve the NVFP4 build with `--trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on 4 × B200 or more 3. With SGLang use `--reasoning-parser nemotron_3`, not the BF16 card's `nemotron_v3`, which SGLang doesn't register 4. On SGLang with tools, pass `chat_template_kwargs` with `enable_thinking` true and `force_nonempty_content` true, as the card says, or reasoning and calls won't both parse 5. Thinking is on by default. Pass `enable_thinking: false` for short tasks or `medium_effort: true` to cut reasoning tokens 6. Use `temperature=1.0` and `top_p=0.95`, as the generation config sets ## Questions ### Which is better for AI agents, Hy-Memory-30B-A3B or Nemotron 3 Ultra 550B A55B? Nemotron 3 Ultra 550B A55B scores 69.3 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 3 of 4 scored categories. Hy-Memory-30B-A3B leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-ultra-550b-a55b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-ultra-550b-a55b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "hy-memory-30b-a3b", "b": "nemotron-3-ultra-550b-a55b"}`. 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