{ "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": "llama.cpp scores 60.6 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 of 7 scored categories. HyperQwen leads on schema \u0026 documentation.", "b": { "slug": "llama-cpp", "name": "llama.cpp", "vendor": "ggml.ai (Hugging Face)", "vendorUrl": "https://llama.app", "kind": "http-api", "category": "local-ai", "summary": "Open-source C/C++ engine for running GGUF models locally, with a web interface and compatible model APIs.", "url": "https://www.anchorterminal.com/tools/llama-cpp", "markdownUrl": "https://www.anchorterminal.com/tools/llama-cpp.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/llama-cpp.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/llama-cpp.json", "repo": "https://github.com/ggml-org/llama.cpp", "license": "MIT", "transports": [ "http" ], "packages": [ { "registry": "oci", "name": "ghcr.io/ggml-org/llama.cpp" }, { "registry": "pypi", "name": "gguf" } ], "auth": "none", "authNotes": "No credential by default. `--api-key` (one key or a comma-separated list) or `--api-key-file` (one key a line) turns on a check for every route but /health and the web UI's files, with the key sent as `Authorization: Bearer` or `X-Api-Key`, never in the query string. Keys have no scopes and change only with a restart. TLS is built in with `--ssl-key-file` and `--ssl-cert-file`. The server binds 127.0.0.1:8080 by default, and CORS reflects any Origin with credentials allowed unless built-in tools, MCP servers or `--agent` are on, when it narrows to localhost (https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md).", "pricing": "free", "pricingNotes": "Free under MIT, with no account, key or card. Nothing is sold. You pay for your own hardware and electricity.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-03).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 130200, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-03" }, "docsUrl": "https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md", "capabilities": [ "inference.local", "inference.open-weights", "embed.text", "rerank", "inference.decision", "agent.mcp-client" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "openai-compatible", "docker", "pre-1.0", "no-telemetry" ], "lastRelease": "2026-09-23", "graded": true, "anchor": { "graded": true, "score": 60.6, "grade": "C", "agentReady": false, "rank": 590, "ranked": true, "rankOf": 1239, "categoryRank": 6, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 73, "maintenance": 81, "payments": 60, "reliability": 64, "schema": 47, "security": 52, "transparency": 64 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-03" }, "negative": -1, "negativeNotes": [ "2026-03-26. GHSA-j8rj-fmpv-wcxw (CVE-2026-34159, 9.8 at NVD), unauthenticated code execution through a GRAPH_COMPUTE bypass in the RPC backend, the most serious of four advisories published between January and March 2026 (the others a llama-server out-of-bounds write through a negative `n_discard` and two GGUF integer overflows). All were fixed in named builds and published as advisories, SECURITY.md says not to expose the RPC server or llama-server to untrusted networks, and the newest is more than six months old, -1. https://github.com/ggml-org/llama.cpp/security/advisories/GHSA-j8rj-fmpv-wcxw; https://github.com/ggml-org/llama.cpp/security" ], "verdict": "MIT, with no telemetry or update check in the source, and `--offline` blocks model downloads. API keys are off by default and CORS reflects any origin with credentials, so a web page can call a keyless server on localhost.", "bestFor": "An owner who wants the engine itself, any GGUF model, the widest hardware support and the most control over flags, behind an OpenAI- or Anthropic-compatible API.", "strengths": [ "MIT, with no telemetry or update check in the source, and `--offline` blocks model downloads", "OpenAI chat completions, responses and embeddings, Anthropic messages, reranking and /v1/systemone from one server", "`response_fields`, `json_schema` and `grammar` control the size and shape of output, and errors carry an OpenAI-style type and code", "1,005 nightly builds and eight semver releases in 90 days, with 37 workflows running on every push to master", "Ten published GitHub advisories with CVEs and fixed builds, and SECURITY.md guidance on untrusted models and inputs" ], "weaknesses": [ "API keys are off by default and CORS reflects any origin with credentials, so a web page can call a keyless server on localhost", "No OpenAPI file of its own, and the REST API changelog stops at b4599", "Private security disclosure disabled since 1 June 2026, with fixes asked for as public pull requests", "Pre-1.0 (0.5.0), and semver releases are bare tags with no notes", "No official client library, and `n_predict` defaults to unlimited" ], "agentNotes": [ "Start the server with `--api-key` and `--cors-origins localhost` before anything else can reach the port. Both are off by default", "Pass `n_predict` or `max_tokens`. Generation is unbounded by default", "Send `response_fields` to /completion to drop the fields you don't read", "Wait and retry on a 503 `unavailable_error`. The model is still loading", "Read the server README of the build you run. Behaviour changes between nightly builds without a changelog entry" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 2, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.6 } ], "editorialScores": { "ergonomics": 73, "maintenance": 81, "payments": 60, "reliability": 64, "schema": 47, "security": 52, "transparency": 66 }, "provenanceScore": 61, "checked": { "categories": { "ergonomics": "2026-10-03", "maintenance": "2026-10-03", "payments": "2026-10-03", "reliability": "2026-10-03", "schema": "2026-10-03", "security": "2026-10-03", "transparency": "2026-10-03" }, "last": "2026-10-03", "lastMoved": "2026-10-27", "oldest": "2026-10-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "curl -LsSf https://llama.app/install.sh | sh # or: brew install llama.cpp; winget install llama.cpp\nllama serve -hf ggml-org/Qwen3.5-0.8B-GGUF # listens on 127.0.0.1:8080", "http": "curl --request POST \\\n --url http://localhost:8080/completion \\\n --header \"Content-Type: application/json\" \\\n --data '{\"prompt\": \"Building a website can be done in 10 simple steps:\",\"n_predict\": 128}'" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/llama-cpp" }, "area": "models", "provenance": { "legalEntity": "ggml.ai, part of Hugging Face since 2026", "domain": "llama.app", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/ggml-org/llama.cpp/releases", "securityTxt": "none", "checked": "2026-10-03", "notes": [ "The repository's About link is llama.app, which says it's by the llama.cpp team and Hugging Face and links no terms, privacy or security page. ggml.ai says the company was acquired by Hugging Face in 2026 and names no address.", "The `LICENSE` file reads Copyright (c) 2023-2026 The ggml authors.", "llama.app/.well-known/security.txt and llama.app/llms.txt return 404. SECURITY.md points to GitHub private advisories while saying private disclosure is disabled.", "There's no shared hosted endpoint. The server runs on the owner's machine." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/llama-cpp.json", "live": { "slug": "llama-cpp", "versions": [ { "registry": "github", "name": "ggml-org/llama.cpp", "version": "v0.8.0", "released": "2026-10-28", "seenAt": "2026-10-30T14:24:41.466725947Z" }, { "registry": "pypi", "name": "gguf", "version": "0.19.0", "released": "2026-05-06", "seenAt": "2026-10-30T14:24:41.347590947Z" } ], "githubStars": 132840, "pypiWeekly": 1187825, "securityTxt": { "url": "https://llama.app/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-30T15:37:42.23807599Z" }, "domain": { "domain": "llama.app", "registered": "2018-07-18", "source": "https://pubapi.registry.google/rdap/domain/llama.app", "checkedAt": "2026-10-25T13:52:28.504541792Z" }, "github": { "repo": "ggml-org/llama.cpp", "checkedAt": "2026-10-26T07:29:50.837961545Z", "stars": 132385, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-26T07:09:27Z", "openIssues": 777, "openPRs": 1806, "issuesOpened90d": 1463, "issuesClosed90d": 1358, "prsMerged90d": 1725, "ci": { "branch": "master", "runs": 50, "success": 42, "failure": 8, "other": 0, "latest": "success", "latestAt": "2026-10-25T23:34:15Z" }, "advisories": [ { "ghsa": "GHSA-j8rj-fmpv-wcxw", "cve": "CVE-2026-34159", "severity": "critical", "published": "2026-03-26T09:59:34Z" }, { "ghsa": "GHSA-96jg-mvhq-q7q7", "cve": "CVE-2026-33298", "severity": "high", "published": "2026-03-18T18:37:01Z" }, { "ghsa": "GHSA-3p4r-fq3f-q74v", "cve": "CVE-2026-27940", "severity": "high", "published": "2026-03-12T11:17:52Z" }, { "ghsa": "GHSA-8947-pfff-2f3c", "cve": "CVE-2026-21869", "severity": "low", "published": "2026-01-05T14:00:09Z" }, { "ghsa": "GHSA-vgg9-87g3-85w8", "cve": "CVE-2025-53630", "severity": "high", "published": "2025-07-10T05:22:33Z" }, { "ghsa": "GHSA-g4cc-763q-h9h6", "severity": "medium", "published": "2025-06-25T06:26:17Z" }, { "ghsa": "GHSA-7rxv-5jhh-j6xx", "cve": "CVE-2025-52566", "severity": "high", "published": "2025-06-23T16:04:05Z" }, { "ghsa": "GHSA-8wwf-w4qm-gpqr", "cve": "CVE-2025-49847", "severity": "high", "published": "2025-06-14T09:55:24Z" }, { "ghsa": "GHSA-wcr5-566p-9cwj", "cve": "CVE-2024-42479", "severity": "critical", "published": "2024-08-12T14:18:18Z" }, { "ghsa": "GHSA-5vm9-p64x-gqw9", "cve": "CVE-2024-42478", "severity": "medium", "published": "2024-08-12T14:18:08Z" }, { "ghsa": "GHSA-mqp6-7pv6-fqjf", "cve": "CVE-2024-42477", "severity": "medium", "published": "2024-08-12T14:17:57Z" }, { "ghsa": "GHSA-49q7-2jmh-92fp", "cve": "CVE-2024-41130", "severity": "medium", "published": "2024-07-22T16:22:34Z" }, { "ghsa": "GHSA-p5mv-gjc5-mwqv", "cve": "CVE-2024-32878", "severity": "high", "published": "2024-04-26T10:17:10Z" } ], "replies": { "sampled": 5, "unanswered": 4, "medianDays": 0.5 } }, "updatedAt": "2026-10-30T15:37:42.23807599Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "syv.ai (syv-ai on GitHub)", "b": "ggml.ai (Hugging Face)", "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": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "none", "b": "2026-09-23", "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": "130k stars", "name": "Popularity" }, { "a": "none", "b": "2.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "llama.cpp scores 60.6 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 of 7 scored categories. HyperQwen leads on schema \u0026 documentation.", "question": "Which is better for AI agents, HyperQwen or llama.cpp?" }, { "answer": "Neither needs a key.", "question": "Do HyperQwen and llama.cpp need an API key?" }, { "answer": "No hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for llama.cpp.", "question": "Can an agent call HyperQwen and llama.cpp without installing anything?" }, { "answer": "Yes. HyperQwen is open source (Apache-2.0). llama.cpp is open source (MIT).", "question": "Are HyperQwen and llama.cpp open source?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 56 against 47" ], "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, 64 against 59", "Agent ergonomics, 73 against 65", "Security \u0026 auth, 52 against 41", "Maintenance \u0026 community, 81 against 68", "Transparency \u0026 trust, 64 against 55" ], "also": null, "goodFor": "An owner who wants the engine itself, any GGUF model, the widest hardware support and the most control over flags, behind an OpenAI- or Anthropic-compatible API.", "slug": "llama-cpp", "watchFor": "API keys are off by default and CORS reflects any origin with credentials, so a web page can call a keyless server on localhost" } ], "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-llama-cpp.json", "title": "AnythingLLM vs llama.cpp", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-llama-cpp" }, { "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-llama-cpp.json", "title": "Berth vs llama.cpp", "url": "https://www.anchorterminal.com/compare/berth-vs-llama-cpp" }, { "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-llama-cpp.json", "title": "BitRun vs llama.cpp", "url": "https://www.anchorterminal.com/compare/bitrun-vs-llama-cpp" }, { "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-llama-cpp.json", "title": "Docker Model Runner vs llama.cpp", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-llama-cpp" }, { "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-llama-cpp.json", "title": "Foundry Local vs llama.cpp", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-llama-cpp" }, { "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-llama-cpp.json", "title": "Core vs llama.cpp", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-llama-cpp" }, { "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-llama-cpp.json", "title": "GPT4All vs llama.cpp", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-llama-cpp" }, { "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-lemonade.json", "title": "HyperQwen vs Lemonade", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade" }, { "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-llama-cpp.json", "title": "Jan vs llama.cpp", "url": "https://www.anchorterminal.com/compare/jan-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/khoj-vs-llama-cpp.json", "title": "Khoj vs llama.cpp", "url": "https://www.anchorterminal.com/compare/khoj-vs-llama-cpp" }, { "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/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/llama-cpp-vs-lm-studio.json", "title": "llama.cpp vs LM Studio", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-lm-studio" }, { "json": "https://www.anchorterminal.com/compare/llama-cpp-vs-localai.json", "title": "llama.cpp vs LocalAI", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/llama-cpp-vs-mlx-lm.json", "title": "llama.cpp vs MLX LM", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/llama-cpp-vs-ollama.json", "title": "llama.cpp vs Ollama", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/llama-cpp-vs-open-webui.json", "title": "llama.cpp vs Open WebUI", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/llama-cpp-vs-screenpipe.json", "title": "llama.cpp vs screenpipe", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/llama-cpp-vs-text-generation-webui.json", "title": "llama.cpp vs TextGen", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/llama-cpp-vs-vllm.json", "title": "llama.cpp vs vLLM", "url": "https://www.anchorterminal.com/compare/llama-cpp-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/llama-cpp-vs-underdog.json", "title": "llama.cpp vs Underdog", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-underdog" } ], "scores": [ { "by": 5, "edge": "llama-cpp", "hyperqwen": 59, "key": "reliability", "llama-cpp": 64, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 9, "edge": "hyperqwen", "hyperqwen": 56, "key": "schema", "llama-cpp": 47, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 8, "edge": "llama-cpp", "hyperqwen": 65, "key": "ergonomics", "llama-cpp": 73, "name": "Agent ergonomics", "weight": 13 }, { "by": 11, "edge": "llama-cpp", "hyperqwen": 41, "key": "security", "llama-cpp": 52, "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "edge": "", "hyperqwen": 60, "key": "payments", "llama-cpp": 60, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 13, "edge": "llama-cpp", "hyperqwen": 68, "key": "maintenance", "llama-cpp": 81, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 9, "edge": "llama-cpp", "hyperqwen": 55, "key": "transparency", "llama-cpp": 64, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "llama.cpp scores 60.6 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 of 7 scored categories. HyperQwen leads on schema \u0026 documentation. 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.", "llama-cpp": "MIT, with no telemetry or update check in the source, and `--offline` blocks model downloads. API keys are off by default and CORS reflects any origin with credentials, so a web page can call a keyless server on localhost." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp", "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.min.md" }, "markdown": "llama.cpp scores 60.6 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 of 7 scored categories. HyperQwen leads on schema \u0026 documentation. 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- llama.cpp: grade C, 60.6/100, rank #590 of 1239. Markdown https://www.anchorterminal.com/tools/llama-cpp.md · JSON https://www.anchorterminal.com/api/v1/tools/llama-cpp.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- Schema \u0026 documentation, 56 against 47\n\nWatch for: Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap\n\n### llama.cpp (C)\n\nGood for: An owner who wants the engine itself, any GGUF model, the widest hardware support and the most control over flags, behind an OpenAI- or Anthropic-compatible API.\n\nAhead on:\n- Reliability, 64 against 59\n- Agent ergonomics, 73 against 65\n- Security \u0026 auth, 52 against 41\n- Maintenance \u0026 community, 81 against 68\n- Transparency \u0026 trust, 64 against 55\n\nWatch for: API keys are off by default and CORS reflects any origin with credentials, so a web page can call a keyless server on localhost\n\n\n## Score by category\n\n| Category | Weight | HyperQwen | llama.cpp | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 59 | 64 | llama.cpp +5 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 56 | 47 | HyperQwen +9 |\n| Agent ergonomics | 13% (16.2 this run) | 65 | 73 | llama.cpp +8 |\n| Security \u0026 auth | 14% (17.5 this run) | 41 | 52 | llama.cpp +11 |\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 | 81 | llama.cpp +13 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 55 | 64 | llama.cpp +9 |\n| Negative events | ≤15 | 0 | -1 | |\n| **Total** | | **56.9 · C** | **60.6 · C** | |\n\n## Facts side by side\n\n| Fact | HyperQwen | llama.cpp |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | syv.ai (syv-ai on GitHub) | ggml.ai (Hugging Face) |\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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | none | 2026-09-23 |\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 | 130k stars |\n| Agent reviews | none | 2.5/5 (2) |\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**llama.cpp.** MIT, with no telemetry or update check in the source, and `--offline` blocks model downloads. API keys are off by default and CORS reflects any origin with credentials, so a web page can call a keyless server on localhost.\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### llama.cpp\n\n1. Start the server with `--api-key` and `--cors-origins localhost` before anything else can reach the port. Both are off by default\n2. Pass `n_predict` or `max_tokens`. Generation is unbounded by default\n3. Send `response_fields` to /completion to drop the fields you don't read\n4. Wait and retry on a 503 `unavailable_error`. The model is still loading\n5. Read the server README of the build you run. Behaviour changes between nightly builds without a changelog entry\n\n## Questions\n\n### Which is better for AI agents, HyperQwen or llama.cpp?\n\nllama.cpp scores 60.6 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 of 7 scored categories. HyperQwen leads on schema \u0026 documentation.\n\n### Do HyperQwen and llama.cpp need an API key?\n\nNeither needs a key.\n\n### Can an agent call HyperQwen and llama.cpp without installing anything?\n\nNo hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for llama.cpp.\n\n### Are HyperQwen and llama.cpp open source?\n\nYes. HyperQwen is open source (Apache-2.0). llama.cpp is open source (MIT).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hyperqwen\", \"b\": \"llama-cpp\"}`. From a terminal: `anchor compare hyperqwen llama-cpp`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hyperqwen.json and https://www.anchorterminal.com/api/v1/tools/llama-cpp.json\n\n## Other comparisons with HyperQwen or llama.cpp\n\n- [AnythingLLM vs HyperQwen](https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.md)\n- [AnythingLLM vs llama.cpp](https://www.anchorterminal.com/compare/anythingllm-vs-llama-cpp.md)\n- [Berth vs HyperQwen](https://www.anchorterminal.com/compare/berth-vs-hyperqwen.md)\n- [Berth vs llama.cpp](https://www.anchorterminal.com/compare/berth-vs-llama-cpp.md)\n- [BitRun vs HyperQwen](https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen.md)\n- [BitRun vs llama.cpp](https://www.anchorterminal.com/compare/bitrun-vs-llama-cpp.md)\n- [Docker Model Runner vs HyperQwen](https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.md)\n- [Docker Model Runner vs llama.cpp](https://www.anchorterminal.com/compare/docker-model-runner-vs-llama-cpp.md)\n- [Foundry Local vs HyperQwen](https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen.md)\n- [Foundry Local vs llama.cpp](https://www.anchorterminal.com/compare/foundry-local-vs-llama-cpp.md)\n- [Core vs HyperQwen](https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen.md)\n- [Core vs llama.cpp](https://www.anchorterminal.com/compare/ghost-core-vs-llama-cpp.md)\n- [GPT4All vs HyperQwen](https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.md)\n- [GPT4All vs llama.cpp](https://www.anchorterminal.com/compare/gpt4all-vs-llama-cpp.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 Lemonade](https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.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 llama.cpp](https://www.anchorterminal.com/compare/jan-vs-llama-cpp.md)\n- [Khoj vs llama.cpp](https://www.anchorterminal.com/compare/khoj-vs-llama-cpp.md)\n- [KoboldCpp vs llama.cpp](https://www.anchorterminal.com/compare/koboldcpp-vs-llama-cpp.md)\n- [Lemonade vs llama.cpp](https://www.anchorterminal.com/compare/lemonade-vs-llama-cpp.md)\n- [llama.cpp vs LM Studio](https://www.anchorterminal.com/compare/llama-cpp-vs-lm-studio.md)\n- [llama.cpp vs LocalAI](https://www.anchorterminal.com/compare/llama-cpp-vs-localai.md)\n- [llama.cpp vs MLX LM](https://www.anchorterminal.com/compare/llama-cpp-vs-mlx-lm.md)\n- [llama.cpp vs Ollama](https://www.anchorterminal.com/compare/llama-cpp-vs-ollama.md)\n- [llama.cpp vs Open WebUI](https://www.anchorterminal.com/compare/llama-cpp-vs-open-webui.md)\n- [llama.cpp vs screenpipe](https://www.anchorterminal.com/compare/llama-cpp-vs-screenpipe.md)\n- [llama.cpp vs TextGen](https://www.anchorterminal.com/compare/llama-cpp-vs-text-generation-webui.md)\n- [llama.cpp vs vLLM](https://www.anchorterminal.com/compare/llama-cpp-vs-vllm.md)\n- [HyperQwen vs Underdog](https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.md)\n- [llama.cpp vs Underdog](https://www.anchorterminal.com/compare/llama-cpp-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 llama.cpp", "url": "" } ], "description": "llama.cpp scores 60.6 (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", "llama.cpp C 60.6", "scores" ], "h1": "HyperQwen vs llama.cpp", "image": "https://www.anchorterminal.com/assets/og/compare-hyperqwen-vs-llama-cpp.png", "path": "/compare/hyperqwen-vs-llama-cpp", "published": "2026-10-01", "section": "tools", "title": "HyperQwen vs llama.cpp for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp" }, "tokens": { "markdown": 2650, "slim": 530 }, "version": 1 }