{ "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": "KoboldCpp scores 61 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 3 of 7 scored categories.", "b": { "slug": "koboldcpp", "name": "KoboldCpp", "vendor": "LostRuins (Concedo)", "vendorUrl": "https://koboldcpp.net", "kind": "http-api", "category": "local-ai", "summary": "Open-source program for running GGUF models on the owner's own computer, built on llama.cpp. One executable serves a web interface and KoboldAI, OpenAI, Ollama and Anthropic compatible APIs on port 5001.", "url": "https://www.anchorterminal.com/tools/koboldcpp", "markdownUrl": "https://www.anchorterminal.com/tools/koboldcpp.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/koboldcpp.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/koboldcpp.json", "repo": "https://github.com/LostRuins/koboldcpp", "license": "AGPL-3.0 for KoboldCpp and KoboldAI Lite. The bundled GGML, llama.cpp and stable-diffusion.cpp code stays under MIT", "transports": [ "http" ], "packages": [ { "registry": "oci", "name": "koboldai/koboldcpp" } ], "auth": "none", "authNotes": "No credential by default. `--password` (or the `KCPP_PASSWORD` environment variable) sets one shared key, sent as `Authorization: Bearer`, and the server reads it from the header only. The key guards text routes. Image generation, upscaling, `/sdapi/v1/interrogate` and `/tts_to_audio` skip the check, and the `--help` text says image endpoints are not secured. Admin routes need `--admin`, an `--admindir` and, when set, a separate `--adminpassword`. Keys have no scopes and change only with a restart. With no `--host` the server listens on all routable interfaces, and CORS reflects any Origin with credentials allowed (https://github.com/LostRuins/koboldcpp/blob/concedo/koboldcpp.py).", "pricing": "free", "pricingNotes": "Free under AGPL-3.0, with no account, key or card. Nothing is sold by the project. The owner pays for hardware and electricity, and the README links third-party GPU rental (RunPod, SimplePod) and Google Colab as other places to run it.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the README, the wiki, the API document or `koboldcpp.py` (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 11972, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://github.com/LostRuins/koboldcpp/wiki", "llmsTxt": "https://koboldcpp.net/llms.txt", "openapi": "https://lite.koboldai.net/koboldcpp_api.json", "capabilities": [ "inference.local", "inference.open-weights", "agent.mcp-client", "embed.text", "image.generate", "speech.stt", "speech.tts", "audio.music" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "openai-compatible", "openapi", "llms-txt", "docker", "agpl", "no-telemetry" ], "lastRelease": "2026-09-27", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 570, "ranked": true, "rankOf": 1239, "categoryRank": 4, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 63, "maintenance": 82, "payments": 60, "reliability": 68, "schema": 68, "security": 38, "transparency": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "One file runs text, image, speech and music models behind a published OpenAPI 3.0.3 document, with eight releases in 90 days. The server listens on every interface with no password by default, and `--password` leaves the image routes open.", "bestFor": "An owner who wants text, image, speech and music models behind one executable with a writing and roleplay interface, and clients that speak the KoboldAI, OpenAI, Ollama or Anthropic formats.", "strengths": [ "OpenAPI 3.0.3 document with 54 operations, served by the program at `/api?json=1` and published at lite.koboldai.net", "KoboldAI, OpenAI, Ollama, Anthropic, AUTOMATIC1111 and ComfyUI style routes from one server on port 5001", "Eight releases between 10 July and 27 September 2026, each with written notes, and replies on all 24 of the newest open issues", "AGPL-3.0, with no telemetry or update check found in `koboldcpp.py` and a wiki statement that inputs are sent nowhere", "Admin functions are off by default and take their own `--adminpassword`" ], "weaknesses": [ "With no `--host` the server accepts connections on all routable interfaces, and no password is set by default", "`--password` covers text routes only. The `--help` text says image endpoints are not secured", "CORS reflects any Origin with credentials allowed and permits private-network requests", "No `SECURITY.md` or security.txt, and the one advisory (GHSA-qhvp-gj7g-rw26) still lists no patched version", "No continuous test run on pushes. Build workflows are started by hand and the only automatic test covers `AutoGuess.json`" ], "agentNotes": [ "Start with `--host 127.0.0.1` and `--password`. The default listens on every interface with no key", "Send the password as `Authorization: Bearer \u003cpassword\u003e`. It is not read from the query string", "Treat 503 as both busy and rate limited. The server never sends 429 or `Retry-After`, and the wait in seconds is in `detail.msg`", "Pass `max_length` or `max_tokens`. The default is 2,048 tokens unless `--defaultgenamt` changes it", "Send a `genkey` with each generation so `/api/extra/generate/check` and `/api/extra/abort` act on your request and not another caller's" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "ergonomics": 63, "maintenance": 82, "payments": 60, "reliability": 68, "schema": 68, "security": 38, "transparency": 70 }, "provenanceScore": 39, "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": "curl -fLo koboldcpp-linux-x64 https://github.com/LostRuins/koboldcpp/releases/latest/download/koboldcpp-linux-x64 \u0026\u0026 chmod +x koboldcpp-linux-x64 \u0026\u0026 ./koboldcpp-linux-x64\n./koboldcpp-linux-x64 --model /path/to/model.gguf # listens on port 5001", "http": "curl --request POST \\\n --url http://localhost:5001/api/v1/generate \\\n --header \"Content-Type: application/json\" \\\n --data '{\"prompt\": \"Niko the kobold stalked carefully down the alley,\", \"max_context_length\": 2048, \"max_length\": 100}'" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/koboldcpp" }, "area": "models", "provenance": { "legalEntity": "", "domain": "koboldcpp.net", "domainRegistered": "2026-03-18", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/LostRuins/koboldcpp/releases", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "No legal entity is named in the repository, the wiki or koboldcpp.net. The maintainer publishes as LostRuins on GitHub and Concedo on Discord, and the Windows binary's version resource gives KoboldAI as the company name.", "The project publishes no terms of service and no privacy policy, so both fields are empty. The licence is AGPL-3.0 and the only privacy statement is an FAQ entry in the wiki.", "The README calls koboldcpp.net the official community website. RDAP gives its registration date as 2026-03-18 and Cloudflare, Inc. as registrar. The README warns that koboldcpp.com is a fake site.", "koboldcpp.net/.well-known/security.txt and koboldai.org/.well-known/security.txt return 404. koboldcpp.net/llms.txt returns a short index that links llms-small.txt and llms-full.txt.", "There's no shared hosted endpoint. The server runs on the owner's machine. The online API reference is on lite.koboldai.net." ], "score": 39 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/koboldcpp.json", "live": { "slug": "koboldcpp", "versions": [ { "registry": "github", "name": "LostRuins/koboldcpp", "version": "v1.123.1", "released": "2026-10-13", "seenAt": "2026-10-30T14:22:28.620759793Z" } ], "githubStars": 12098, "securityTxt": { "url": "https://koboldcpp.net/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-30T15:38:29.545828635Z" }, "llmsTxt": { "url": "https://koboldcpp.net/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-30T15:07:46.943922999Z" }, "domain": { "domain": "koboldcpp.net", "registered": "2026-03-18", "source": "https://rdap.verisign.com/net/v1/domain/koboldcpp.net", "checkedAt": "2026-10-25T14:01:05.024363126Z" }, "github": { "repo": "lostruins/koboldcpp", "checkedAt": "2026-10-26T07:41:06.308004134Z", "stars": 12067, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-25T15:46:06Z", "openIssues": 528, "openPRs": 9, "issuesOpened90d": 107, "issuesClosed90d": 65, "prsMerged90d": 51, "advisories": [ { "ghsa": "GHSA-h59g-8cq4-cqjj", "severity": "high", "published": "2026-10-13T13:48:50Z" }, { "ghsa": "GHSA-qhvp-gj7g-rw26", "severity": "low", "published": "2026-08-29T03:32:25Z" } ], "replies": { "sampled": 25, "unanswered": 4, "medianDays": 0.2 } }, "updatedAt": "2026-10-30T15:38:29.545828635Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "syv.ai (syv-ai on GitHub)", "b": "LostRuins (Concedo)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "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": "AGPL-3.0 for KoboldCpp and KoboldAI Lite. The bundled GGML, llama.cpp and stable-diffusion.cpp code stays under MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "none", "b": "2026-09-27", "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": "12k stars", "name": "Popularity" }, { "a": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "KoboldCpp scores 61 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 3 of 7 scored categories.", "question": "Which is better for AI agents, HyperQwen or KoboldCpp?" }, { "answer": "Neither needs a key.", "question": "Do HyperQwen and KoboldCpp need an API key?" }, { "answer": "No hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for KoboldCpp.", "question": "Can an agent call HyperQwen and KoboldCpp without installing anything?" }, { "answer": "Yes. HyperQwen is open source (Apache-2.0). KoboldCpp is open source (AGPL-3.0 for KoboldCpp and KoboldAI Lite. The bundled GGML, llama.cpp and stable-diffusion.cpp code stays under MIT).", "question": "Are HyperQwen and KoboldCpp open source?" } ], "goodFor": [ { "aheadOn": null, "also": null, "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, 68 against 59", "Schema \u0026 documentation, 68 against 56", "Maintenance \u0026 community, 82 against 68" ], "also": null, "goodFor": "An owner who wants text, image, speech and music models behind one executable with a writing and roleplay interface, and clients that speak the KoboldAI, OpenAI, Ollama or Anthropic formats.", "slug": "koboldcpp", "watchFor": "With no `--host` the server accepts connections on all routable interfaces, and no password is set by default" } ], "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-koboldcpp.json", "title": "AnythingLLM vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-koboldcpp" }, { "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-koboldcpp.json", "title": "Berth vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/berth-vs-koboldcpp" }, { "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-koboldcpp.json", "title": "BitRun vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/bitrun-vs-koboldcpp" }, { "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-koboldcpp.json", "title": "Docker Model Runner vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-koboldcpp" }, { "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-koboldcpp.json", "title": "Foundry Local vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-koboldcpp" }, { "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-koboldcpp.json", "title": "Core vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-koboldcpp" }, { "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-koboldcpp.json", "title": "GPT4All vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-koboldcpp" }, { "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-lemonade.json", "title": "HyperQwen vs Lemonade", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade" }, { "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-koboldcpp.json", "title": "Jan vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/jan-vs-koboldcpp" }, { "json": "https://www.anchorterminal.com/compare/khoj-vs-koboldcpp.json", "title": "Khoj vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/khoj-vs-koboldcpp" }, { "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/koboldcpp-vs-llama-cpp.json", "title": "KoboldCpp vs llama.cpp", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-lm-studio.json", "title": "KoboldCpp vs LM Studio", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-lm-studio" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-localai.json", "title": "KoboldCpp vs LocalAI", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-mlx-lm.json", "title": "KoboldCpp vs MLX LM", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-ollama.json", "title": "KoboldCpp vs Ollama", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-open-webui.json", "title": "KoboldCpp vs Open WebUI", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-screenpipe.json", "title": "KoboldCpp vs screenpipe", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-text-generation-webui.json", "title": "KoboldCpp vs TextGen", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-vllm.json", "title": "KoboldCpp vs vLLM", "url": "https://www.anchorterminal.com/compare/koboldcpp-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/koboldcpp-vs-underdog.json", "title": "KoboldCpp vs Underdog", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-underdog" } ], "scores": [ { "by": 9, "edge": "koboldcpp", "hyperqwen": 59, "key": "reliability", "koboldcpp": 68, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 12, "edge": "koboldcpp", "hyperqwen": 56, "key": "schema", "koboldcpp": 68, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 2, "edge": "hyperqwen", "hyperqwen": 65, "key": "ergonomics", "koboldcpp": 63, "name": "Agent ergonomics", "weight": 13 }, { "by": 3, "edge": "hyperqwen", "hyperqwen": 41, "key": "security", "koboldcpp": 38, "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "edge": "", "hyperqwen": 60, "key": "payments", "koboldcpp": 60, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 14, "edge": "koboldcpp", "hyperqwen": 68, "key": "maintenance", "koboldcpp": 82, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 0, "edge": "", "hyperqwen": 55, "key": "transparency", "koboldcpp": 55, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "KoboldCpp scores 61 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 3 of 7 scored categories. 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.", "koboldcpp": "One file runs text, image, speech and music models behind a published OpenAPI 3.0.3 document, with eight releases in 90 days. The server listens on every interface with no password by default, and `--password` leaves the image routes open." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp", "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.min.md" }, "markdown": "KoboldCpp scores 61 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 3 of 7 scored categories. 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- KoboldCpp: grade C, 61/100, rank #570 of 1239. Markdown https://www.anchorterminal.com/tools/koboldcpp.md · JSON https://www.anchorterminal.com/api/v1/tools/koboldcpp.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\nWatch for: Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap\n\n### KoboldCpp (C)\n\nGood for: An owner who wants text, image, speech and music models behind one executable with a writing and roleplay interface, and clients that speak the KoboldAI, OpenAI, Ollama or Anthropic formats.\n\nAhead on:\n- Reliability, 68 against 59\n- Schema \u0026 documentation, 68 against 56\n- Maintenance \u0026 community, 82 against 68\n\nWatch for: With no `--host` the server accepts connections on all routable interfaces, and no password is set by default\n\n\n## Score by category\n\n| Category | Weight | HyperQwen | KoboldCpp | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 59 | 68 | KoboldCpp +9 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 56 | 68 | KoboldCpp +12 |\n| Agent ergonomics | 13% (16.2 this run) | 65 | 63 | HyperQwen +2 |\n| Security \u0026 auth | 14% (17.5 this run) | 41 | 38 | HyperQwen +3 |\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 | 82 | KoboldCpp +14 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 55 | 55 | even |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.9 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | HyperQwen | KoboldCpp |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | syv.ai (syv-ai on GitHub) | LostRuins (Concedo) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | AGPL-3.0 for KoboldCpp and KoboldAI Lite. The bundled GGML, llama.cpp and stable-diffusion.cpp code stays under MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | none | 2026-09-27 |\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 | 12k stars |\n| Agent reviews | none | 2.8/5 (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**KoboldCpp.** One file runs text, image, speech and music models behind a published OpenAPI 3.0.3 document, with eight releases in 90 days. The server listens on every interface with no password by default, and `--password` leaves the image routes open.\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### KoboldCpp\n\n1. Start with `--host 127.0.0.1` and `--password`. The default listens on every interface with no key\n2. Send the password as `Authorization: Bearer \u003cpassword\u003e`. It is not read from the query string\n3. Treat 503 as both busy and rate limited. The server never sends 429 or `Retry-After`, and the wait in seconds is in `detail.msg`\n4. Pass `max_length` or `max_tokens`. The default is 2,048 tokens unless `--defaultgenamt` changes it\n5. Send a `genkey` with each generation so `/api/extra/generate/check` and `/api/extra/abort` act on your request and not another caller's\n\n## Questions\n\n### Which is better for AI agents, HyperQwen or KoboldCpp?\n\nKoboldCpp scores 61 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 3 of 7 scored categories.\n\n### Do HyperQwen and KoboldCpp need an API key?\n\nNeither needs a key.\n\n### Can an agent call HyperQwen and KoboldCpp without installing anything?\n\nNo hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for KoboldCpp.\n\n### Are HyperQwen and KoboldCpp open source?\n\nYes. HyperQwen is open source (Apache-2.0). KoboldCpp is open source (AGPL-3.0 for KoboldCpp and KoboldAI Lite. The bundled GGML, llama.cpp and stable-diffusion.cpp code stays under MIT).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hyperqwen\", \"b\": \"koboldcpp\"}`. From a terminal: `anchor compare hyperqwen koboldcpp`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hyperqwen.json and https://www.anchorterminal.com/api/v1/tools/koboldcpp.json\n\n## Other comparisons with HyperQwen or KoboldCpp\n\n- [AnythingLLM vs HyperQwen](https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.md)\n- [AnythingLLM vs KoboldCpp](https://www.anchorterminal.com/compare/anythingllm-vs-koboldcpp.md)\n- [Berth vs HyperQwen](https://www.anchorterminal.com/compare/berth-vs-hyperqwen.md)\n- [Berth vs KoboldCpp](https://www.anchorterminal.com/compare/berth-vs-koboldcpp.md)\n- [BitRun vs HyperQwen](https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen.md)\n- [BitRun vs KoboldCpp](https://www.anchorterminal.com/compare/bitrun-vs-koboldcpp.md)\n- [Docker Model Runner vs HyperQwen](https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.md)\n- [Docker Model Runner vs KoboldCpp](https://www.anchorterminal.com/compare/docker-model-runner-vs-koboldcpp.md)\n- [Foundry Local vs HyperQwen](https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen.md)\n- [Foundry Local vs KoboldCpp](https://www.anchorterminal.com/compare/foundry-local-vs-koboldcpp.md)\n- [Core vs HyperQwen](https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen.md)\n- [Core vs KoboldCpp](https://www.anchorterminal.com/compare/ghost-core-vs-koboldcpp.md)\n- [GPT4All vs HyperQwen](https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.md)\n- [GPT4All vs KoboldCpp](https://www.anchorterminal.com/compare/gpt4all-vs-koboldcpp.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 Lemonade](https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.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 KoboldCpp](https://www.anchorterminal.com/compare/jan-vs-koboldcpp.md)\n- [Khoj vs KoboldCpp](https://www.anchorterminal.com/compare/khoj-vs-koboldcpp.md)\n- [KoboldCpp vs Lemonade](https://www.anchorterminal.com/compare/koboldcpp-vs-lemonade.md)\n- [KoboldCpp vs llama.cpp](https://www.anchorterminal.com/compare/koboldcpp-vs-llama-cpp.md)\n- [KoboldCpp vs LM Studio](https://www.anchorterminal.com/compare/koboldcpp-vs-lm-studio.md)\n- [KoboldCpp vs LocalAI](https://www.anchorterminal.com/compare/koboldcpp-vs-localai.md)\n- [KoboldCpp vs MLX LM](https://www.anchorterminal.com/compare/koboldcpp-vs-mlx-lm.md)\n- [KoboldCpp vs Ollama](https://www.anchorterminal.com/compare/koboldcpp-vs-ollama.md)\n- [KoboldCpp vs Open WebUI](https://www.anchorterminal.com/compare/koboldcpp-vs-open-webui.md)\n- [KoboldCpp vs screenpipe](https://www.anchorterminal.com/compare/koboldcpp-vs-screenpipe.md)\n- [KoboldCpp vs TextGen](https://www.anchorterminal.com/compare/koboldcpp-vs-text-generation-webui.md)\n- [KoboldCpp vs vLLM](https://www.anchorterminal.com/compare/koboldcpp-vs-vllm.md)\n- [HyperQwen vs Underdog](https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.md)\n- [KoboldCpp vs Underdog](https://www.anchorterminal.com/compare/koboldcpp-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 KoboldCpp", "url": "" } ], "description": "KoboldCpp scores 61 (C) to HyperQwen's 56.9 (C) for local inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "HyperQwen C 56.9", "KoboldCpp C 61", "scores" ], "h1": "HyperQwen vs KoboldCpp", "image": "https://www.anchorterminal.com/assets/og/compare-hyperqwen-vs-koboldcpp.png", "path": "/compare/hyperqwen-vs-koboldcpp", "published": "2026-10-01", "section": "tools", "title": "HyperQwen vs KoboldCpp for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp" }, "tokens": { "markdown": 2700, "slim": 530 }, "version": 1 }