{ "data": { "a": { "slug": "hyperqwen", "name": "HyperQwen", "vendor": "syv.ai (syv-ai on GitHub)", "vendorUrl": "https://syv.ai", "kind": "http-api", "category": "local-ai", "summary": "HyperQwen is syv.ai's open-source serving setup for Qwen3.8-27B on one 24 GB NVIDIA GPU. A patch series against a pinned vLLM, a requantisation pipeline and Docker Compose profiles run vLLM's OpenAI-compatible API on port 18020.", "url": "https://www.anchorterminal.com/tools/hyperqwen", "markdownUrl": "https://www.anchorterminal.com/tools/hyperqwen.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hyperqwen.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hyperqwen.json", "repo": "https://github.com/syv-ai/HyperQwen", "license": "Apache-2.0", "transports": [ "http" ], "packages": [ { "registry": "oci", "name": "ghcr.io/syv-ai/hyperqwen" } ], "auth": "none", "authNotes": "No credential by default. `VLLM_API_KEY` in `.env` (or an `api_key.txt` file) turns on vLLM's Bearer check. With no key, the launcher binds 127.0.0.1 outside a container, and Docker Compose publishes the port on 127.0.0.1 unless `BIND` is changed. With a key set, the launcher binds every interface. The key is vLLM's own, one static key with no scopes, which vLLM checks only under `/v1`, `/v2`, `/inference` and `/cohere` (per the vLLM listing's check of 9 October 2026, https://github.com/vllm-project/vllm/blob/main/docs/usage/security.md).", "pricing": "free", "pricingNotes": "Free under Apache-2.0 with no account, key or card. Nothing is sold. The README links a Ko-fi page for donations towards GPU time. The owner pays for a 24 GB NVIDIA GPU, about 40 GB of disk and electricity.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the README, the docs or the launch scripts (checked 2026-10-30). Nothing is sold.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 2400, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-30" }, "docsUrl": "https://github.com/syv-ai/HyperQwen#readme", "capabilities": [ "inference.local", "inference.open-weights" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "apache-2.0", "openai-compatible", "docker", "tool-calling", "reasoning", "pre-1.0" ], "graded": true, "anchor": { "graded": true, "score": 56.9, "grade": "C", "agentReady": false, "rank": 738, "ranked": true, "rankOf": 1239, "categoryRank": 11, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 65, "maintenance": 68, "payments": 60, "reliability": 59, "schema": 56, "security": 41, "transparency": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-30" }, "negative": 0, "verdict": "HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy.", "bestFor": "An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.", "strengths": [ "Prebuilt image on ghcr.io with an immutable tag per commit, built by CI that applies all 60 patches and runs the build-time tests", "Tool calling on by default with the `qwen3_coder` parser and argument schemas enforced (`TOOL_STRICT=parameter`)", "With no API key the server listens on 127.0.0.1 only, and a CI check tests that default", "Five documented setups from 64k to 240k context, with the measured cost of each knob", "Apache-2.0 throughout, with vendored kernels and patches carrying their upstream references" ], "weaknesses": [ "Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap", "No tags, releases or changelog. Changes reach users through `latest` on every push to main", "No SECURITY.md or disclosure route, and the base weights come from a third-party repository with no pinned revision", "vLLM's key covers only `/v1`-style prefixes, so control routes such as `/pause` answer without it", "First start pulls a 9.5 GB image and about 20 GB of weights and can take over 30 minutes" ], "agentNotes": [ "Call http://127.0.0.1:18020/v1 with model `qwen3.8-27b` and the `VLLM_API_KEY` value, or any string if no key was set", "Poll `/health` before the first request. A first start downloads and prepares the model and compiles kernels, which can take over 30 minutes", "Declare the context window your setup uses (65,536 by default, 131,072 for `CTX=long`, 245,760 for `CTX=huge`) so the client doesn't truncate", "Ask the owner to set `VLLM_API_KEY` before setting `BIND=0.0.0.0`, and to front the port with a proxy that allowlists routes", "Treat the README's tokens-per-second figures as the project's measurements on one RTX 3090 at 250 W" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.9 } ], "editorialScores": { "ergonomics": 65, "maintenance": 68, "payments": 60, "reliability": 59, "schema": 56, "security": 41, "transparency": 60 }, "provenanceScore": 49, "checked": { "categories": { "ergonomics": "2026-10-30", "maintenance": "2026-10-30", "payments": "2026-10-30", "reliability": "2026-10-30", "schema": "2026-10-30", "security": "2026-10-30", "transparency": "2026-10-30" }, "last": "2026-10-30", "oldest": "2026-10-30", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "git clone https://github.com/syv-ai/HyperQwen \u0026\u0026 cd HyperQwen\ncp .env.example .env\n\ndocker compose --profile single up -d # one or a few people chatting\ndocker compose --profile batch up -d # API backend, many concurrent requests" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/hyperqwen" }, "area": "models", "provenance": { "legalEntity": "syv.ai (Copenhagen, CVR DK44671247)", "domain": "syv.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://syv.ai/privatlivspolitik", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-30", "notes": [ "The Hugging Face organisation syvai, full name syv.ai, links https://syv.ai and github.com/syv-ai, and hosts the drafter files HyperQwen downloads. syv.ai's own pages don't mention HyperQwen.", "syv.ai's contact page gives the address in Copenhagen and CVR DK44671247. The Danish CVR register answered with a bot check and wasn't read.", "No terms, SECURITY.md or changelog for HyperQwen. The Apache-2.0 licence stands in for terms. The privacy policy listed is syv.ai's website policy and doesn't cover the software, which runs on the owner's machine.", "syv.ai answers 404 at /.well-known/security.txt and /security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hyperqwen.json" }, "answer": "Lemonade scores 63.9 (B) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 of 7 scored categories. HyperQwen leads on security \u0026 auth.", "b": { "slug": "lemonade", "name": "Lemonade", "vendor": "AMD and the Lemonade community", "vendorUrl": "https://lemonade-server.ai", "kind": "http-api", "category": "local-ai", "summary": "Open-source local AI server from AMD and community contributors. It runs text, speech and image models on the owner's CPU, GPU or NPU behind OpenAI-, Anthropic- and Ollama-compatible APIs and an MCP endpoint on port 13305.", "url": "https://www.anchorterminal.com/tools/lemonade", "markdownUrl": "https://www.anchorterminal.com/tools/lemonade.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lemonade.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lemonade.json", "repo": "https://github.com/lemonade-sdk/lemonade", "license": "Apache 2.0. Each backend (llama.cpp, whisper.cpp, stable-diffusion.cpp, FastFlowLM and others) is downloaded separately under its own licence", "transports": [ "http" ], "packages": [ { "registry": "oci", "name": "ghcr.io/lemonade-sdk/lemonade-server" } ], "auth": "api-key", "authNotes": "Off by default. With no key set, every endpoint answers without authentication, on a default bind of localhost. `LEMONADE_API_KEY` sets one bearer key for the regular API (`/api/*`, `/v0/*`, `/v1/*`, `/mcp` and `/metrics`). `LEMONADE_ADMIN_API_KEY` sets a second key for the internal control endpoints (`/internal/*`), and without it the regular key reaches those too. Both are environment variables, so there are no per-user keys. The docs tell WebSocket clients to pass `?api_key=KEY` in the URL.", "pricing": "free", "pricingNotes": "Free and Apache 2.0 with nothing to buy and no account. You run it on your own hardware. Cloud Offload, which is optional and experimental, bills through the owner's own keys at whichever cloud provider they add.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-08).", "endpoints": [] }, "toolCount": 6, "popularity": { "githubStars": 5800, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://lemonade-server.ai/docs/", "capabilities": [ "inference.local", "inference.open-weights", "embed.text", "rerank", "speech.stt", "speech.tts", "image.generate" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "openai-compatible", "mcp", "streaming", "open-weights", "amd", "npu", "docker" ], "lastRelease": "2026-10-07", "graded": true, "anchor": { "graded": true, "score": 63.9, "grade": "B", "agentReady": false, "rank": 430, "ranked": true, "rankOf": 1239, "categoryRank": 3, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 70, "maintenance": 76, "payments": 60, "reliability": 75, "schema": 70, "security": 36, "transparency": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "Apache 2.0, with weekly releases, installers for Windows, macOS and five Linux routes, and a six-tool MCP endpoint whose descriptions steer a caller away from accidental multi-gigabyte downloads. Authentication is off by default, the repository has no security policy, and the docs tell WebSocket clients to pass the key in the URL.", "bestFor": "An owner with AMD hardware (Ryzen AI NPUs, Radeon or Strix Halo) who wants one local server for chat, speech and images that existing OpenAI, Anthropic or Ollama clients can call, and for MCP clients that want local models as tools.", "strengths": [ "Apache 2.0, with OpenAI, Anthropic Messages, Ollama and llama.cpp-compatible routes on one port (13305)", "`POST /mcp` exposes six tools over Streamable HTTP, and omitting `model` reuses a loaded or downloaded model before any download", "Every running server returns its own Markdown API reference at `GET /v1/docs` and through the `lemonade_docs` tool, so it matches the installed version", "Stable release v2026.41.1 on 7 October 2026 on a weekly cadence, each with a Breaking Changes section in its notes", "Telemetry is off by default and exports OTLP traces only to an endpoint the operator sets, with switches to redact prompts and outputs" ], "weaknesses": [ "No authentication by default. With no key set, every route answers, including `/internal/*` shutdown and configuration", "GitHub reports no `SECURITY.md`, and we found no security.txt, advisory or disclosure address", "The docs tell WebSocket clients to send the key as `?api_key=KEY` in the URL", "No OpenAPI file in the repository, and no `readOnlyHint` or `destructiveHint` on the MCP tools", "The main build and test workflow's badge read failing on main on 8 October 2026, with 411 issues open" ], "agentNotes": [ "Call `lemonade_list_models` (or `GET /v1/models`) before naming a model. A wrong name with `allow_download: true` can start a multi-gigabyte download", "Send `Authorization: Bearer \u003ckey\u003e` when the operator has set `LEMONADE_API_KEY`. `/internal/*` needs the admin key when one is set", "Use `POST /v1/chat/completions` for streamed tokens, embeddings and speech. The MCP endpoint ignores `stream` and has no embeddings or text-to-speech tool", "Pass `output_dir` to `lemonade_generate_image` and `lemonade_omni` to get file paths in place of inline base64. Writes stay inside the MCP media sandbox", "Read `GET /v1/docs` on the running server for the reference that matches its version. Weekly releases change behaviour" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.9 } ], "editorialScores": { "ergonomics": 70, "maintenance": 76, "payments": 60, "reliability": 75, "schema": 70, "security": 36, "transparency": 70 }, "provenanceScore": 60, "checked": { "categories": { "ergonomics": "2026-10-08", "maintenance": "2026-10-08", "payments": "2026-10-08", "reliability": "2026-10-08", "schema": "2026-10-08", "security": "2026-10-08", "transparency": "2026-10-08" }, "last": "2026-10-08", "lastMoved": "2026-10-28", "oldest": "2026-10-08", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "winget install --id AMD.LemonadeServer -e", "http": "curl http://localhost:13305/api/v1/chat/completions -H \"Content-Type: application/json\" -d '{\"model\": \"your-model-name\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}]}'", "claudeCode": "lemonade launch claude" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/lemonade" }, "area": "models", "provenance": { "legalEntity": "Advanced Micro Devices, Inc.", "domain": "lemonade-server.ai", "domainRegistered": "2025-05-12", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/lemonade-sdk/lemonade/releases", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "The website footer reads \"© 2026 AMD. Licensed under Apache 2.0\", and `contrib/debian/copyright` in the repository names Advanced Micro Devices, Inc. The code lives in the lemonade-sdk organisation on GitHub, and the README calls it a community project with optimisations by AMD engineers.", "We found no terms of service and no privacy policy for the software or the website. The home page and its footer link to neither, so both fields are empty.", "lemonade-server.ai/.well-known/security.txt, /security.txt and /llms.txt return 404.", "RDAP gives a registration date of 2025-05-12 for lemonade-server.ai, with Porkbun LLC as registrar and the registrant behind a privacy service.", "There's no hosted endpoint. Each instance answers on the owner's own machine, by default localhost port 13305." ], "score": 60 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lemonade.json", "live": { "slug": "lemonade", "versions": [ { "registry": "github", "name": "lemonade-sdk/lemonade", "version": "v2026.44.1", "released": "2026-10-28", "seenAt": "2026-10-30T14:23:47.946153441Z" } ], "githubStars": 6025, "securityTxt": { "url": "https://lemonade-server.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-30T15:38:47.171036779Z" }, "domain": { "domain": "lemonade-server.ai", "registered": "2025-05-12", "source": "https://rdap.identitydigital.services/rdap/domain/lemonade-server.ai", "checkedAt": "2026-10-25T13:53:24.779824602Z" }, "github": { "repo": "lemonade-sdk/lemonade", "checkedAt": "2026-10-26T07:39:23.418517625Z", "stars": 5999, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-26T04:52:33Z", "openIssues": 432, "openPRs": 176, "issuesOpened90d": 359, "issuesClosed90d": 250, "prsMerged90d": 493, "ci": { "branch": "main", "runs": 50, "success": 50, "failure": 0, "other": 0, "latest": "success", "latestAt": "2026-10-24T18:42:09Z" }, "replies": { "sampled": 25, "unanswered": 21, "medianDays": 0.9 } }, "updatedAt": "2026-10-30T15:38:47.171036779Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "syv.ai (syv-ai on GitHub)", "b": "AMD and the Lemonade community", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "API key", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache 2.0. Each backend (llama.cpp, whisper.cpp, stable-diffusion.cpp, FastFlowLM and others) is downloaded separately under its own licence", "name": "Licence" }, { "a": "none", "b": "6", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "none", "b": "2026-10-07", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no date given", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.4k stars", "b": "5.8k stars", "name": "Popularity" }, { "a": "none", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Lemonade scores 63.9 (B) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 of 7 scored categories. HyperQwen leads on security \u0026 auth.", "question": "Which is better for AI agents, HyperQwen or Lemonade?" }, { "answer": "HyperQwen needs no key. Lemonade needs an API key.", "question": "Do HyperQwen and Lemonade need an API key?" }, { "answer": "No hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for Lemonade.", "question": "Can an agent call HyperQwen and Lemonade without installing anything?" }, { "answer": "Yes. HyperQwen is open source (Apache-2.0). Lemonade is open source (Apache 2.0. Each backend (llama.cpp, whisper.cpp, stable-diffusion.cpp, FastFlowLM and others) is downloaded separately under its own licence).", "question": "Are HyperQwen and Lemonade open source?" } ], "goodFor": [ { "aheadOn": [ "Security \u0026 auth, 41 against 36" ], "also": [ "No key needed to call it" ], "goodFor": "An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.", "slug": "hyperqwen", "watchFor": "Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap" }, { "aheadOn": [ "Reliability, 75 against 59", "Schema \u0026 documentation, 70 against 56", "Agent ergonomics, 70 against 65", "Maintenance \u0026 community, 76 against 68", "Transparency \u0026 trust, 65 against 55" ], "also": null, "goodFor": "An owner with AMD hardware (Ryzen AI NPUs, Radeon or Strix Halo) who wants one local server for chat, speech and images that existing OpenAI, Anthropic or Ollama clients can call, and for MCP clients that want local models as tools.", "slug": "lemonade", "watchFor": "No authentication by default. With no key set, every route answers, including `/internal/*` shutdown and configuration" } ], "job": { "capability": "inference.local", "name": "Local inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.json", "title": "AnythingLLM vs HyperQwen", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-lemonade.json", "title": "AnythingLLM vs Lemonade", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/berth-vs-hyperqwen.json", "title": "Berth vs HyperQwen", "url": "https://www.anchorterminal.com/compare/berth-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/berth-vs-lemonade.json", "title": "Berth vs Lemonade", "url": "https://www.anchorterminal.com/compare/berth-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen.json", "title": "BitRun vs HyperQwen", "url": "https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-lemonade.json", "title": "BitRun vs Lemonade", "url": "https://www.anchorterminal.com/compare/bitrun-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.json", "title": "Docker Model Runner vs HyperQwen", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-lemonade.json", "title": "Docker Model Runner vs Lemonade", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen.json", "title": "Foundry Local vs HyperQwen", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/foundry-local-vs-lemonade.json", "title": "Foundry Local vs Lemonade", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen.json", "title": "Core vs HyperQwen", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-lemonade.json", "title": "Core vs Lemonade", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.json", "title": "GPT4All vs HyperQwen", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-lemonade.json", "title": "GPT4All vs Lemonade", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-jan.json", "title": "HyperQwen vs Jan", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-khoj.json", "title": "HyperQwen vs Khoj", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-khoj" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.json", "title": "HyperQwen vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.json", "title": "HyperQwen vs llama.cpp", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-lm-studio.json", "title": "HyperQwen vs LM Studio", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-lm-studio" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-localai.json", "title": "HyperQwen vs LocalAI", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm.json", "title": "HyperQwen vs MLX LM", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.json", "title": "HyperQwen vs Ollama", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-open-webui.json", "title": "HyperQwen vs Open WebUI", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-screenpipe.json", "title": "HyperQwen vs screenpipe", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-text-generation-webui.json", "title": "HyperQwen vs TextGen", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.json", "title": "HyperQwen vs vLLM", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-lemonade.json", "title": "Jan vs Lemonade", "url": "https://www.anchorterminal.com/compare/jan-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/khoj-vs-lemonade.json", "title": "Khoj vs Lemonade", "url": "https://www.anchorterminal.com/compare/khoj-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-lemonade.json", "title": "KoboldCpp vs Lemonade", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-llama-cpp.json", "title": "Lemonade vs llama.cpp", "url": "https://www.anchorterminal.com/compare/lemonade-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-lm-studio.json", "title": "Lemonade vs LM Studio", "url": "https://www.anchorterminal.com/compare/lemonade-vs-lm-studio" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-localai.json", "title": "Lemonade vs LocalAI", "url": "https://www.anchorterminal.com/compare/lemonade-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm.json", "title": "Lemonade vs MLX LM", "url": "https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-ollama.json", "title": "Lemonade vs Ollama", "url": "https://www.anchorterminal.com/compare/lemonade-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-open-webui.json", "title": "Lemonade vs Open WebUI", "url": "https://www.anchorterminal.com/compare/lemonade-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-screenpipe.json", "title": "Lemonade vs screenpipe", "url": "https://www.anchorterminal.com/compare/lemonade-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-text-generation-webui.json", "title": "Lemonade vs TextGen", "url": "https://www.anchorterminal.com/compare/lemonade-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-vllm.json", "title": "Lemonade vs vLLM", "url": "https://www.anchorterminal.com/compare/lemonade-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.json", "title": "HyperQwen vs Underdog", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-underdog" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-underdog.json", "title": "Lemonade vs Underdog", "url": "https://www.anchorterminal.com/compare/lemonade-vs-underdog" } ], "scores": [ { "by": 16, "edge": "lemonade", "hyperqwen": 59, "key": "reliability", "lemonade": 75, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 14, "edge": "lemonade", "hyperqwen": 56, "key": "schema", "lemonade": 70, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 5, "edge": "lemonade", "hyperqwen": 65, "key": "ergonomics", "lemonade": 70, "name": "Agent ergonomics", "weight": 13 }, { "by": 5, "edge": "hyperqwen", "hyperqwen": 41, "key": "security", "lemonade": 36, "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "edge": "", "hyperqwen": 60, "key": "payments", "lemonade": 60, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 8, "edge": "lemonade", "hyperqwen": 68, "key": "maintenance", "lemonade": 76, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 10, "edge": "lemonade", "hyperqwen": 55, "key": "transparency", "lemonade": 65, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Lemonade scores 63.9 (B) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 of 7 scored categories. HyperQwen leads on security \u0026 auth. Both do local inference.", "tutorials": [], "verdicts": { "hyperqwen": "HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy.", "lemonade": "Apache 2.0, with weekly releases, installers for Windows, macOS and five Linux routes, and a six-tool MCP endpoint whose descriptions steer a caller away from accidental multi-gigabyte downloads. Authentication is off by default, the repository has no security policy, and the docs tell WebSocket clients to pass the key in the URL." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade", "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.min.md" }, "markdown": "Lemonade scores 63.9 (B) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 of 7 scored categories. HyperQwen leads on security \u0026 auth. Both do local inference.\n\n- HyperQwen: grade C, 56.9/100, rank #738 of 1239. Markdown https://www.anchorterminal.com/tools/hyperqwen.md · JSON https://www.anchorterminal.com/api/v1/tools/hyperqwen.json\n- Lemonade: grade B, 63.9/100, rank #430 of 1239. Markdown https://www.anchorterminal.com/tools/lemonade.md · JSON https://www.anchorterminal.com/api/v1/tools/lemonade.json\n- Best local AI models and assistants: https://www.anchorterminal.com/best/local-ai/index.md\n- All 253 local ai comparisons: https://www.anchorterminal.com/compare/local-ai/index.md\n\n## Which one, for what\n\n### HyperQwen (C)\n\nGood for: An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.\n\nAhead on:\n- Security \u0026 auth, 41 against 36\n\nAlso in its favour:\n- No key needed to call it\n\nWatch for: Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap\n\n### Lemonade (B)\n\nGood for: An owner with AMD hardware (Ryzen AI NPUs, Radeon or Strix Halo) who wants one local server for chat, speech and images that existing OpenAI, Anthropic or Ollama clients can call, and for MCP clients that want local models as tools.\n\nAhead on:\n- Reliability, 75 against 59\n- Schema \u0026 documentation, 70 against 56\n- Agent ergonomics, 70 against 65\n- Maintenance \u0026 community, 76 against 68\n- Transparency \u0026 trust, 65 against 55\n\nWatch for: No authentication by default. With no key set, every route answers, including `/internal/*` shutdown and configuration\n\n\n## Score by category\n\n| Category | Weight | HyperQwen | Lemonade | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 59 | 75 | Lemonade +16 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 56 | 70 | Lemonade +14 |\n| Agent ergonomics | 13% (16.2 this run) | 65 | 70 | Lemonade +5 |\n| Security \u0026 auth | 14% (17.5 this run) | 41 | 36 | HyperQwen +5 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 60 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 68 | 76 | Lemonade +8 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 55 | 65 | Lemonade +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.9 · C** | **63.9 · B** | |\n\n## Facts side by side\n\n| Fact | HyperQwen | Lemonade |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | syv.ai (syv-ai on GitHub) | AMD and the Lemonade community |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | None | API key |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache 2.0. Each backend (llama.cpp, whisper.cpp, stable-diffusion.cpp, FastFlowLM and others) is downloaded separately under its own licence |\n| Tools exposed | none | 6 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | none | 2026-10-07 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no date given | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Popularity | 2.4k stars | 5.8k stars |\n| Agent reviews | none | 3/5 (4) |\n\n## Verdicts\n\n**HyperQwen.** HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy.\n\n**Lemonade.** Apache 2.0, with weekly releases, installers for Windows, macOS and five Linux routes, and a six-tool MCP endpoint whose descriptions steer a caller away from accidental multi-gigabyte downloads. Authentication is off by default, the repository has no security policy, and the docs tell WebSocket clients to pass the key in the URL.\n\n## Before you call either\n\n### HyperQwen\n\n1. Call http://127.0.0.1:18020/v1 with model `qwen3.8-27b` and the `VLLM_API_KEY` value, or any string if no key was set\n2. Poll `/health` before the first request. A first start downloads and prepares the model and compiles kernels, which can take over 30 minutes\n3. Declare the context window your setup uses (65,536 by default, 131,072 for `CTX=long`, 245,760 for `CTX=huge`) so the client doesn't truncate\n4. Ask the owner to set `VLLM_API_KEY` before setting `BIND=0.0.0.0`, and to front the port with a proxy that allowlists routes\n5. Treat the README's tokens-per-second figures as the project's measurements on one RTX 3090 at 250 W\n\n### Lemonade\n\n1. Call `lemonade_list_models` (or `GET /v1/models`) before naming a model. A wrong name with `allow_download: true` can start a multi-gigabyte download\n2. Send `Authorization: Bearer \u003ckey\u003e` when the operator has set `LEMONADE_API_KEY`. `/internal/*` needs the admin key when one is set\n3. Use `POST /v1/chat/completions` for streamed tokens, embeddings and speech. The MCP endpoint ignores `stream` and has no embeddings or text-to-speech tool\n4. Pass `output_dir` to `lemonade_generate_image` and `lemonade_omni` to get file paths in place of inline base64. Writes stay inside the MCP media sandbox\n5. Read `GET /v1/docs` on the running server for the reference that matches its version. Weekly releases change behaviour\n\n## Questions\n\n### Which is better for AI agents, HyperQwen or Lemonade?\n\nLemonade scores 63.9 (B) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 of 7 scored categories. HyperQwen leads on security \u0026 auth.\n\n### Do HyperQwen and Lemonade need an API key?\n\nHyperQwen needs no key. Lemonade needs an API key.\n\n### Can an agent call HyperQwen and Lemonade without installing anything?\n\nNo hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for Lemonade.\n\n### Are HyperQwen and Lemonade open source?\n\nYes. HyperQwen is open source (Apache-2.0). Lemonade is open source (Apache 2.0. Each backend (llama.cpp, whisper.cpp, stable-diffusion.cpp, FastFlowLM and others) is downloaded separately under its own licence).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hyperqwen\", \"b\": \"lemonade\"}`. From a terminal: `anchor compare hyperqwen lemonade`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hyperqwen.json and https://www.anchorterminal.com/api/v1/tools/lemonade.json\n\n## Other comparisons with HyperQwen or Lemonade\n\n- [AnythingLLM vs HyperQwen](https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.md)\n- [AnythingLLM vs Lemonade](https://www.anchorterminal.com/compare/anythingllm-vs-lemonade.md)\n- [Berth vs HyperQwen](https://www.anchorterminal.com/compare/berth-vs-hyperqwen.md)\n- [Berth vs Lemonade](https://www.anchorterminal.com/compare/berth-vs-lemonade.md)\n- [BitRun vs HyperQwen](https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen.md)\n- [BitRun vs Lemonade](https://www.anchorterminal.com/compare/bitrun-vs-lemonade.md)\n- [Docker Model Runner vs HyperQwen](https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.md)\n- [Docker Model Runner vs Lemonade](https://www.anchorterminal.com/compare/docker-model-runner-vs-lemonade.md)\n- [Foundry Local vs HyperQwen](https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen.md)\n- [Foundry Local vs Lemonade](https://www.anchorterminal.com/compare/foundry-local-vs-lemonade.md)\n- [Core vs HyperQwen](https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen.md)\n- [Core vs Lemonade](https://www.anchorterminal.com/compare/ghost-core-vs-lemonade.md)\n- [GPT4All vs HyperQwen](https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.md)\n- [GPT4All vs Lemonade](https://www.anchorterminal.com/compare/gpt4all-vs-lemonade.md)\n- [HyperQwen vs Jan](https://www.anchorterminal.com/compare/hyperqwen-vs-jan.md)\n- [HyperQwen vs Khoj](https://www.anchorterminal.com/compare/hyperqwen-vs-khoj.md)\n- [HyperQwen vs KoboldCpp](https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.md)\n- [HyperQwen vs llama.cpp](https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.md)\n- [HyperQwen vs LM Studio](https://www.anchorterminal.com/compare/hyperqwen-vs-lm-studio.md)\n- [HyperQwen vs LocalAI](https://www.anchorterminal.com/compare/hyperqwen-vs-localai.md)\n- [HyperQwen vs MLX LM](https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm.md)\n- [HyperQwen vs Ollama](https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.md)\n- [HyperQwen vs Open WebUI](https://www.anchorterminal.com/compare/hyperqwen-vs-open-webui.md)\n- [HyperQwen vs screenpipe](https://www.anchorterminal.com/compare/hyperqwen-vs-screenpipe.md)\n- [HyperQwen vs TextGen](https://www.anchorterminal.com/compare/hyperqwen-vs-text-generation-webui.md)\n- [HyperQwen vs vLLM](https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.md)\n- [Jan vs Lemonade](https://www.anchorterminal.com/compare/jan-vs-lemonade.md)\n- [Khoj vs Lemonade](https://www.anchorterminal.com/compare/khoj-vs-lemonade.md)\n- [KoboldCpp vs Lemonade](https://www.anchorterminal.com/compare/koboldcpp-vs-lemonade.md)\n- [Lemonade vs llama.cpp](https://www.anchorterminal.com/compare/lemonade-vs-llama-cpp.md)\n- [Lemonade vs LM Studio](https://www.anchorterminal.com/compare/lemonade-vs-lm-studio.md)\n- [Lemonade vs LocalAI](https://www.anchorterminal.com/compare/lemonade-vs-localai.md)\n- [Lemonade vs MLX LM](https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm.md)\n- [Lemonade vs Ollama](https://www.anchorterminal.com/compare/lemonade-vs-ollama.md)\n- [Lemonade vs Open WebUI](https://www.anchorterminal.com/compare/lemonade-vs-open-webui.md)\n- [Lemonade vs screenpipe](https://www.anchorterminal.com/compare/lemonade-vs-screenpipe.md)\n- [Lemonade vs TextGen](https://www.anchorterminal.com/compare/lemonade-vs-text-generation-webui.md)\n- [Lemonade vs vLLM](https://www.anchorterminal.com/compare/lemonade-vs-vllm.md)\n- [HyperQwen vs Underdog](https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.md)\n- [Lemonade vs Underdog](https://www.anchorterminal.com/compare/lemonade-vs-underdog.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-31", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "HyperQwen vs Lemonade", "url": "" } ], "description": "Lemonade scores 63.9 (B) to HyperQwen's 56.9 (C) for local inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "HyperQwen C 56.9", "Lemonade B 63.9", "scores" ], "h1": "HyperQwen vs Lemonade", "image": "https://www.anchorterminal.com/assets/og/compare-hyperqwen-vs-lemonade.png", "path": "/compare/hyperqwen-vs-lemonade", "published": "2026-10-01", "section": "tools", "title": "HyperQwen vs Lemonade for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade" }, "tokens": { "markdown": 2850, "slim": 630 }, "version": 1 }