{ "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": "vLLM scores 58 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 of 7 scored categories.", "b": { "slug": "vllm", "name": "vLLM", "vendor": "vLLM project (PyTorch Foundation)", "vendorUrl": "https://vllm.ai", "kind": "http-api", "category": "local-ai", "summary": "vLLM is an open-source inference and serving engine for open-weight language models. `vllm serve` runs an HTTP server with OpenAI-compatible, Anthropic Messages, embedding, reranking and transcription routes on the owner's own GPUs or CPUs.", "url": "https://www.anchorterminal.com/tools/vllm", "markdownUrl": "https://www.anchorterminal.com/tools/vllm.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/vllm.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/vllm.json", "repo": "https://github.com/vllm-project/vllm", "license": "Apache-2.0", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "vllm" }, { "registry": "oci", "name": "vllm/vllm-openai" } ], "auth": "none", "authNotes": "No credential by default. `--api-key` (one or several keys) or `VLLM_API_KEY` turns on a Bearer check for paths under `/v1`, `/v2`, `/inference` and `/cohere` only, so `/invocations`, `/pooling`, `/classify`, `/score`, `/rerank` and control routes such as `/pause` stay open. Keys have no scopes and change with a restart. The key is read from the `Authorization` header, never the query string. gRPC has no authentication (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 by the project. 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 2026-10-09).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 93444, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-09" }, "docsUrl": "https://docs.vllm.ai/en/stable/", "capabilities": [ "inference.local", "inference.open-weights", "embed.text", "rerank", "speech.stt", "inference.decision", "agent.mcp-client" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "openai-compatible", "docker", "pre-1.0", "telemetry-default-on" ], "lastRelease": "2026-10-02", "graded": true, "anchor": { "graded": true, "score": 58, "grade": "C", "agentReady": false, "rank": 695, "ranked": true, "rankOf": 1239, "categoryRank": 9, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 64, "maintenance": 88, "payments": 60, "reliability": 62, "schema": 68, "security": 50, "transparency": 71 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-09" }, "negative": -6, "negativeNotes": [ "2026-06-02. GHSA-94f4-hr76-p5j6 (CVE-2026-48746, 9.1), a crafted Host header bypassed the API key check on the OpenAI routes, fixed in 0.22.0. With GHSA-4r2x-xpjr-7cvv (CVE-2026-22778, 9.8) of 2 February 2026, code execution through video decoding fixed in 0.14.1, these are the two critical advisories of the last 12 months. Both were fixed and published with CVEs, so they decay, -3. https://github.com/vllm-project/vllm/security/advisories/GHSA-94f4-hr76-p5j6; https://github.com/vllm-project/vllm/security/advisories/GHSA-4r2x-xpjr-7cvv", "2026-10-06. GHSA-h3rc-6mm3-gc2m (8.1), a request field could select the processor code a server started with `--trust-remote-code` imports, fixed in 0.31.0, one of 50 advisories published since 11 July 2026 (10 high, 36 medium, 4 low), most of them requests that crash or exhaust the engine. All name a fixed version, and eleven were published on 9 October 2026 months after their fixes, -3. https://github.com/vllm-project/vllm/security/advisories/GHSA-h3rc-6mm3-gc2m; https://github.com/vllm-project/vllm/security/advisories" ], "verdict": "Apache-2.0 software with a release about every two weeks, each with notes that list breaking changes and security fixes. The optional API key covers only some path prefixes, so `/invocations` and control routes such as `/pause` answer without it, and at least 81 security advisories were published in the 12 months to 9 October 2026.", "bestFor": "An owner with a GPU server who wants many concurrent requests against one open-weight model behind OpenAI or Anthropic compatible routes.", "strengths": [ "OpenAI chat, completions, responses and embeddings, Anthropic `/v1/messages`, Cohere embed and rerank, transcription and `/v1/systemone` from one server", "Apache-2.0, with a written three-stage deprecation policy and release notes that carry a breaking changes section", "Eight stable releases between 12 July and 2 October 2026, and v0.31.0 lists 717 commits from 307 contributors", "A 650-line security guide names every route the API key does and does not protect, and the limits of multi-tenant use", "Usage statistics are documented field by field, with `VLLM_NO_USAGE_STATS`, `DO_NOT_TRACK` or a file as opt-outs" ], "weaknesses": [ "`--api-key` guards only the `/v1`, `/v2`, `/inference` and `/cohere` prefixes. `/invocations`, `/pooling`, `/classify`, `/score`, `/rerank`, `/pause` and `/update_weights` answer without it", "No key by default, the server binds every interface when `--host` is unset, and CORS allows any origin", "At least 81 GitHub security advisories in 12 months, two critical, most of them remote crashes or resource exhaustion", "Pre-1.0 (0.31.0), with breaking changes in each fortnightly release and compatibility kept for a limited number of minor versions", "Usage statistics are sent to stats.vllm.ai by default, and no privacy policy or retention period for them was found" ], "agentNotes": [ "Put a reverse proxy that allowlists routes in front of the server. `--api-key` leaves `/invocations` and the control routes open", "Pass `--host 127.0.0.1` for single-machine use. With no `--host` the server listens on every interface", "Set `VLLM_NO_USAGE_STATS=1` or `DO_NOT_TRACK=1` before starting if nothing should be sent to stats.vllm.ai", "Start with `--enable-auto-tool-choice` and the `--tool-call-parser` for the model before sending tools. Tool calling is off without them", "Send `max_tokens` on every request, and read the breaking changes section of the release notes before upgrading a minor version" ], "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": 58 } ], "editorialScores": { "ergonomics": 64, "maintenance": 88, "payments": 60, "reliability": 62, "schema": 68, "security": 50, "transparency": 80 }, "provenanceScore": 61, "checked": { "categories": { "ergonomics": "2026-10-09", "maintenance": "2026-10-09", "payments": "2026-10-09", "reliability": "2026-10-09", "schema": "2026-10-09", "security": "2026-10-09", "transparency": "2026-10-09" }, "last": "2026-10-09", "lastMoved": "2026-10-28", "oldest": "2026-10-09", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "uv pip install vllm --torch-backend=auto\nvllm serve Qwen/Qwen2.5-1.5B-Instruct # listens on port 8000", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"Qwen/Qwen2.5-1.5B-Instruct\",\n \"messages\": [\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\"role\": \"user\", \"content\": \"Who won the world series in 2020?\"}\n ]\n }'", "claudeCode": "ANTHROPIC_BASE_URL=http://localhost:8000 \\\nANTHROPIC_API_KEY=dummy \\\nANTHROPIC_AUTH_TOKEN=dummy \\\nANTHROPIC_DEFAULT_OPUS_MODEL=my-model \\\nANTHROPIC_DEFAULT_SONNET_MODEL=my-model \\\nANTHROPIC_DEFAULT_HAIKU_MODEL=my-model \\\nclaude" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/vllm" }, "area": "models", "provenance": { "legalEntity": "The Linux Foundation (vLLM is a PyTorch Foundation project)", "domain": "vllm.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/vllm-project/vllm/releases", "securityTxt": "none", "checked": "2026-10-09", "notes": [ "vllm.ai links no terms and no privacy policy, and its footer reads © 2026 vLLM. The project publishes none for the software or for stats.vllm.ai, so the Apache-2.0 licence stands in for terms.", "pytorch.org/projects/vllm/ lists vLLM among PyTorch Foundation projects and says UC Berkeley contributed it to the Linux Foundation in July 2024. The Linux Foundation's policies are linked from that page and are not specific to vLLM.", "vllm.ai/.well-known/security.txt and docs.vllm.ai/.well-known/security.txt return 404. SECURITY.md asks for private reports through GitHub.", "There's no shared hosted endpoint. The server runs on the owner's hardware. The software posts usage statistics to stats.vllm.ai unless turned off." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/vllm.json", "live": { "slug": "vllm", "versions": [ { "registry": "github", "name": "vllm-project/vllm", "version": "v0.32.0", "released": "2026-10-18", "seenAt": "2026-10-30T14:54:02.98934897Z" }, { "registry": "pypi", "name": "vllm", "version": "0.32.0", "released": "2026-10-18", "seenAt": "2026-10-30T14:54:02.866027124Z" } ], "githubStars": 95914, "pypiWeekly": 644498, "securityTxt": { "url": "https://vllm.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-30T15:38:42.335723549Z" }, "domain": { "domain": "vllm.ai", "registered": "2023-06-19", "source": "https://rdap.identitydigital.services/rdap/domain/vllm.ai", "checkedAt": "2026-10-25T13:51:38.019213292Z" }, "github": { "repo": "vllm-project/vllm", "checkedAt": "2026-10-26T08:16:16.30413937Z", "stars": 95553, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-26T07:59:01Z", "openIssues": 2721, "openPRs": 6737, "issuesOpened90d": 2459, "issuesClosed90d": 1772, "prsMerged90d": 4617, "ci": { "branch": "main", "runs": 50, "success": 48, "failure": 0, "other": 2, "latest": "success", "latestAt": "2026-10-26T08:03:00Z" }, "advisories": [ { "ghsa": "GHSA-vvpg-rrww-wpmh", "severity": "low", "published": "2026-10-20T07:37:49Z" }, { "ghsa": "GHSA-8397-8qf6-f2gm", "severity": "medium", "published": "2026-10-20T07:37:27Z" }, { "ghsa": "GHSA-gq5q-89c7-vhvx", "severity": "medium", "published": "2026-10-20T07:37:11Z" }, { "ghsa": "GHSA-h3rc-j82p-2c3h", "severity": "high", "published": "2026-10-20T07:36:56Z" }, { "ghsa": "GHSA-6757-hggm-hp2w", "severity": "high", "published": "2026-10-20T07:36:30Z" }, { "ghsa": "GHSA-frvg-qw88-hww9", "cve": "CVE-2026-94624", "severity": "medium", "published": "2026-10-20T07:35:45Z" }, { "ghsa": "GHSA-q97c-p368-75c4", "severity": "medium", "published": "2026-10-20T07:35:17Z" }, { "ghsa": "GHSA-x567-49g5-p4cf", "severity": "medium", "published": "2026-10-20T07:34:11Z" }, { "ghsa": "GHSA-542m-9234-8gvf", "severity": "medium", "published": "2026-10-20T07:33:26Z" }, { "ghsa": "GHSA-w9r6-29q4-pvpr", "severity": "medium", "published": "2026-10-09T08:27:08Z" }, { "ghsa": "GHSA-4m6g-h9mv-wqjc", "severity": "high", "published": "2026-10-09T08:24:22Z" }, { "ghsa": "GHSA-34qw-q2h2-jp3m", "severity": "medium", "published": "2026-10-09T08:21:43Z" }, { "ghsa": "GHSA-mm38-5g96-7j6x", "severity": "high", "published": "2026-10-09T08:12:07Z" }, { "ghsa": "GHSA-vfp2-c8pq-v6h6", "severity": "high", "published": "2026-10-09T07:58:03Z" }, { "ghsa": "GHSA-r45p-7qwx-qwjw", "severity": "medium", "published": "2026-10-09T07:55:16Z" }, { "ghsa": "GHSA-w2v2-5662-8v5q", "severity": "medium", "published": "2026-10-09T07:49:41Z" }, { "ghsa": "GHSA-6pgw-f3pv-4h8q", "severity": "medium", "published": "2026-10-09T07:47:33Z" }, { "ghsa": "GHSA-rwfw-xvg3-3937", "severity": "medium", "published": "2026-10-09T07:47:04Z" }, { "ghsa": "GHSA-8c65-hq7q-r7jm", "severity": "high", "published": "2026-10-09T07:34:18Z" }, { "ghsa": "GHSA-rhcx-5729-88vg", "severity": "high", "published": "2026-10-09T06:39:02Z" }, { "ghsa": "GHSA-6cxc-2vcg-w5qc", "severity": "low", "published": "2026-10-06T20:45:08Z" }, { "ghsa": "GHSA-p92p-rxj5-7p2x", "cve": "CVE-2026-114050", "severity": "medium", "published": "2026-10-06T07:10:22Z" }, { "ghsa": "GHSA-rh6f-3x46-j33q", "severity": "medium", "published": "2026-10-06T07:08:41Z" }, { "ghsa": "GHSA-h3rc-6mm3-gc2m", "severity": "high", "published": "2026-10-06T07:06:53Z" }, { "ghsa": "GHSA-gx7p-2j49-hfq4", "severity": "high", "published": "2026-10-06T07:04:54Z" }, { "ghsa": "GHSA-823j-m4cj-hmjf", "cve": "CVE-2026-114052", "severity": "high", "published": "2026-10-06T06:59:51Z" }, { "ghsa": "GHSA-4xqp-c3mv-qff7", "severity": "medium", "published": "2026-10-06T06:58:15Z" }, { "ghsa": "GHSA-ph3r-5jfg-f84f", "cve": "CVE-2026-105753", "severity": "medium", "published": "2026-09-28T07:37:56Z" }, { "ghsa": "GHSA-m52c-39rh-f3gp", "severity": "medium", "published": "2026-09-28T07:35:57Z" }, { "ghsa": "GHSA-935w-9g4m-p28p", "cve": "CVE-2026-105752", "severity": "low", "published": "2026-09-28T07:34:45Z" }, { "ghsa": "GHSA-x6mc-67gf-chw4", "cve": "CVE-2026-105758", "severity": "medium", "published": "2026-09-23T10:25:42Z" }, { "ghsa": "GHSA-5fj9-pfhr-6j48", "cve": "CVE-2026-105759", "severity": "medium", "published": "2026-09-23T10:23:16Z" }, { "ghsa": "GHSA-58v5-2m8f-94pr", "cve": "CVE-2026-105760", "severity": "medium", "published": "2026-09-23T10:20:52Z" }, { "ghsa": "GHSA-ph72-cqr5-qpp7", "cve": "CVE-2026-105754", "severity": "medium", "published": "2026-09-23T10:17:44Z" }, { "ghsa": "GHSA-85xf-c7hm-whqw", "cve": "CVE-2026-105757", "severity": "medium", "published": "2026-09-23T10:16:15Z" }, { "ghsa": "GHSA-2phq-3phc-84px", "cve": "CVE-2026-105755", "severity": "medium", "published": "2026-09-23T10:15:01Z" }, { "ghsa": "GHSA-2823-qmq8-rwvj", "cve": "CVE-2026-105756", "severity": "medium", "published": "2026-09-23T10:12:17Z" }, { "ghsa": "GHSA-hhv2-872h-628q", "severity": "medium", "published": "2026-09-12T14:47:49Z" }, { "ghsa": "GHSA-v5gm-qgmv-gc6c", "severity": "medium", "published": "2026-09-12T14:45:55Z" }, { "ghsa": "GHSA-qff2-492f-9fm4", "severity": "medium", "published": "2026-09-12T14:43:57Z" }, { "ghsa": "GHSA-3mqx-f33v-vgp9", "severity": "medium", "published": "2026-09-12T08:56:01Z" }, { "ghsa": "GHSA-j682-9xp5-rrf3", "cve": "CVE-2026-100649", "severity": "low", "published": "2026-09-12T08:54:27Z" }, { "ghsa": "GHSA-p6g9-7v3x-m8mv", "severity": "medium", "published": "2026-09-12T08:52:51Z" }, { "ghsa": "GHSA-jcq2-4gch-5qhf", "cve": "CVE-2026-100648", "severity": "medium", "published": "2026-09-12T08:51:06Z" }, { "ghsa": "GHSA-wpww-v874-ph2p", "cve": "CVE-2026-100647", "severity": "medium", "published": "2026-09-12T08:49:34Z" }, { "ghsa": "GHSA-25q3-v2hm-8vpf", "cve": "CVE-2026-93592", "severity": "high", "published": "2026-09-03T18:08:31Z" }, { "ghsa": "GHSA-8pw2-6jv3-mj5j", "cve": "CVE-2026-69147", "severity": "medium", "published": "2026-08-28T18:09:24Z" }, { "ghsa": "GHSA-3c86-2m5g-59q7", "cve": "CVE-2026-90553", "severity": "high", "published": "2026-08-28T18:05:36Z" }, { "ghsa": "GHSA-936p-m5pv-vvjf", "cve": "CVE-2026-90554", "severity": "medium", "published": "2026-08-28T18:04:03Z" }, { "ghsa": "GHSA-99f2-hwrc-gvq8", "cve": "CVE-2026-90555", "severity": "medium", "published": "2026-08-28T18:03:19Z" }, { "ghsa": "GHSA-hcwq-8wjf-3gcr", "cve": "CVE-2026-57173", "severity": "medium", "published": "2026-08-23T07:21:14Z" }, { "ghsa": "GHSA-4hhp-h66f-j5j7", "cve": "CVE-2026-73560", "severity": "medium", "published": "2026-08-13T15:46:24Z" }, { "ghsa": "GHSA-cqm8-jxg6-fqfq", "cve": "CVE-2026-78684", "severity": "medium", "published": "2026-08-11T06:42:07Z" }, { "ghsa": "GHSA-7m6h-x95x-82q5", "cve": "CVE-2026-73558", "severity": "medium", "published": "2026-08-11T06:40:00Z" }, { "ghsa": "GHSA-pr7f-p5mw-fc87", "cve": "CVE-2026-73557", "severity": "medium", "published": "2026-07-27T09:21:25Z" }, { "ghsa": "GHSA-48jh-3gj7-fg8v", "cve": "CVE-2026-73556", "severity": "medium", "published": "2026-07-27T09:20:51Z" }, { "ghsa": "GHSA-hwrm-c4cx-rf4j", "cve": "CVE-2026-73555", "severity": "low", "published": "2026-07-27T09:19:03Z" }, { "ghsa": "GHSA-8737-qx52-hjff", "cve": "CVE-2026-71486", "severity": "medium", "published": "2026-07-27T09:18:41Z" }, { "ghsa": "GHSA-87x5-vmc3-756j", "cve": "CVE-2026-73559", "severity": "medium", "published": "2026-07-27T09:17:18Z" }, { "ghsa": "GHSA-v82g-2437-67m2", "cve": "CVE-2026-55646", "severity": "medium", "published": "2026-07-02T07:53:16Z" }, { "ghsa": "GHSA-rwxx-mrjm-wc2m", "cve": "CVE-2026-55574", "severity": "medium", "published": "2026-07-02T07:51:56Z" }, { "ghsa": "GHSA-8wr5-jm2h-8r4f", "cve": "CVE-2026-54234", "severity": "high", "published": "2026-07-02T07:50:33Z" }, { "ghsa": "GHSA-33cg-gxv8-3p8g", "cve": "CVE-2026-55514", "severity": "medium", "published": "2026-07-02T07:12:18Z" }, { "ghsa": "GHSA-q8gq-377p-jq3r", "cve": "CVE-2026-41523", "severity": "high", "published": "2026-06-14T08:08:23Z" }, { "ghsa": "GHSA-6pr9-rp53-2pmc", "cve": "CVE-2026-54233", "severity": "medium", "published": "2026-06-11T12:57:00Z" }, { "ghsa": "GHSA-hgg8-fqqc-vfmw", "cve": "CVE-2026-54236", "severity": "medium", "published": "2026-06-11T12:41:26Z" }, { "ghsa": "GHSA-5jv2-g5wq-cmr4", "cve": "CVE-2026-53923", "severity": "medium", "published": "2026-06-11T12:39:47Z" }, { "ghsa": "GHSA-8jr5-v98p-w75m", "cve": "CVE-2026-12491", "severity": "medium", "published": "2026-06-11T12:37:26Z" }, { "ghsa": "GHSA-7h4p-rffg-7823", "cve": "CVE-2026-54235", "severity": "medium", "published": "2026-06-11T12:35:05Z" }, { "ghsa": "GHSA-3ww4-5jv9-j5gm", "cve": "CVE-2026-47155", "severity": "medium", "published": "2026-06-10T14:11:37Z" }, { "ghsa": "GHSA-jrf6-vqxq-pjv2", "cve": "CVE-2026-54232", "severity": "high", "published": "2026-06-09T15:00:23Z" }, { "ghsa": "GHSA-94f4-hr76-p5j6", "cve": "CVE-2026-48746", "severity": "critical", "published": "2026-06-02T16:12:56Z" }, { "ghsa": "GHSA-83vm-p52w-f9pw", "cve": "CVE-2026-44223", "severity": "medium", "published": "2026-04-28T20:03:19Z" }, { "ghsa": "GHSA-hpv8-x276-m59f", "cve": "CVE-2026-44222", "severity": "medium", "published": "2026-04-27T18:00:06Z" }, { "ghsa": "GHSA-pf3h-qjgv-vcpr", "cve": "CVE-2026-34753", "severity": "medium", "published": "2026-04-03T15:23:37Z" }, { "ghsa": "GHSA-pq5c-rjhq-qp7p", "cve": "CVE-2026-34755", "severity": "medium", "published": "2026-04-03T15:10:39Z" }, { "ghsa": "GHSA-3mwp-wvh9-7528", "cve": "CVE-2026-34756", "severity": "medium", "published": "2026-04-03T14:49:03Z" }, { "ghsa": "GHSA-6c4r-fmh3-7rh8", "cve": "CVE-2026-34760", "severity": "medium", "published": "2026-03-30T20:26:30Z" }, { "ghsa": "GHSA-7972-pg2x-xr59", "cve": "CVE-2026-27893", "severity": "high", "published": "2026-03-26T23:48:23Z" }, { "ghsa": "GHSA-v359-jj2v-j536", "cve": "CVE-2026-25960", "severity": "medium", "published": "2026-03-09T17:58:39Z" }, { "ghsa": "GHSA-4r2x-xpjr-7cvv", "cve": "CVE-2026-22778", "severity": "critical", "published": "2026-02-02T14:58:53Z" }, { "ghsa": "GHSA-qh4c-xf7m-gxfc", "cve": "CVE-2026-24779", "severity": "high", "published": "2026-01-27T15:10:43Z" }, { "ghsa": "GHSA-2pc9-4j83-qjmr", "cve": "CVE-2026-22807", "severity": "high", "published": "2026-01-21T15:50:41Z" }, { "ghsa": "GHSA-wv77-2vpf-vmmg", "cve": "CVE-2025-62372", "severity": "medium", "published": "2026-01-21T13:36:26Z" }, { "ghsa": "GHSA-grg2-63fw-f2qr", "cve": "CVE-2026-22773", "severity": "medium", "published": "2026-01-09T19:48:58Z" }, { "ghsa": "GHSA-mcmc-2m55-j8jj", "severity": "high", "published": "2026-01-08T17:02:04Z" }, { "ghsa": "GHSA-8fr4-5q9j-m8gm", "cve": "CVE-2025-66448", "severity": "high", "published": "2025-12-01T19:39:22Z" }, { "ghsa": "GHSA-69j4-grxj-j64p", "cve": "CVE-2025-62426", "severity": "medium", "published": "2025-11-20T19:11:08Z" }, { "ghsa": "GHSA-mrw7-hf4f-83pf", "cve": "CVE-2025-62164", "severity": "high", "published": "2025-11-20T19:03:26Z" }, { "ghsa": "GHSA-pmqf-x6x8-p7qw", "cve": "CVE-2025-62372", "severity": "medium", "published": "2025-11-20T18:58:11Z" }, { "ghsa": "GHSA-6fvq-23cw-5628", "cve": "CVE-2025-61620", "severity": "medium", "published": "2025-10-07T00:24:34Z" }, { "ghsa": "GHSA-3f6c-7fw2-ppm4", "cve": "CVE-2025-6242", "severity": "high", "published": "2025-10-07T00:20:45Z" }, { "ghsa": "GHSA-wr9h-g72x-mwhm", "cve": "CVE-2025-59425", "severity": "high", "published": "2025-10-07T00:18:47Z" }, { "ghsa": "GHSA-rxc4-3w6r-4v47", "cve": "CVE-2025-48956", "severity": "high", "published": "2025-08-20T23:25:54Z" }, { "ghsa": "GHSA-79j6-g2m3-jgfw", "cve": "CVE-2025-9141", "severity": "high", "published": "2025-08-20T23:25:39Z" }, { "ghsa": "GHSA-vrq3-r879-7m65", "cve": "CVE-2025-48944", "severity": "medium", "published": "2025-05-28T17:28:13Z" }, { "ghsa": "GHSA-9hcf-v7m4-6m2j", "cve": "CVE-2025-48943", "severity": "medium", "published": "2025-05-28T17:16:13Z" }, { "ghsa": "GHSA-c65p-x677-fgj6", "cve": "CVE-2025-46722", "severity": "medium", "published": "2025-05-28T17:06:31Z" }, { "ghsa": "GHSA-j828-28rj-hfhp", "severity": "medium", "published": "2025-05-28T17:04:52Z" }, { "ghsa": "GHSA-hjq4-87xh-g4fv", "cve": "CVE-2025-47277", "severity": "critical", "published": "2025-05-20T14:18:47Z" } ] }, "updatedAt": "2026-10-30T15:38:42.335723549Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "syv.ai (syv-ai on GitHub)", "b": "vLLM project (PyTorch Foundation)", "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": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "none", "b": "2026-10-02", "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": "93k stars", "name": "Popularity" } ], "faq": [ { "answer": "vLLM scores 58 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 of 7 scored categories.", "question": "Which is better for AI agents, HyperQwen or vLLM?" }, { "answer": "Neither needs a key.", "question": "Do HyperQwen and vLLM need an API key?" }, { "answer": "No hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for vLLM.", "question": "Can an agent call HyperQwen and vLLM without installing anything?" }, { "answer": "Yes. HyperQwen is open source (Apache-2.0). vLLM is open source (Apache-2.0).", "question": "Are HyperQwen and vLLM open source?" } ], "goodFor": [ { "aheadOn": null, "also": [ "No incidents deducted, where vLLM loses 6 points for them" ], "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": [ "Schema \u0026 documentation, 68 against 56", "Security \u0026 auth, 50 against 41", "Maintenance \u0026 community, 88 against 68", "Transparency \u0026 trust, 71 against 55" ], "also": null, "goodFor": "An owner with a GPU server who wants many concurrent requests against one open-weight model behind OpenAI or Anthropic compatible routes.", "slug": "vllm", "watchFor": "`--api-key` guards only the `/v1`, `/v2`, `/inference` and `/cohere` prefixes. `/invocations`, `/pooling`, `/classify`, `/score`, `/rerank`, `/pause` and `/update_weights` answer without it" } ], "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-vllm.json", "title": "AnythingLLM vs vLLM", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-vllm" }, { "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-vllm.json", "title": "Berth vs vLLM", "url": "https://www.anchorterminal.com/compare/berth-vs-vllm" }, { "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-vllm.json", "title": "BitRun vs vLLM", "url": "https://www.anchorterminal.com/compare/bitrun-vs-vllm" }, { "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-vllm.json", "title": "Docker Model Runner vs vLLM", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-vllm" }, { "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-vllm.json", "title": "Foundry Local vs vLLM", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-vllm" }, { "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-vllm.json", "title": "Core vs vLLM", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-vllm" }, { "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-vllm.json", "title": "GPT4All vs vLLM", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-vllm" }, { "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-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/jan-vs-vllm.json", "title": "Jan vs vLLM", "url": "https://www.anchorterminal.com/compare/jan-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/khoj-vs-vllm.json", "title": "Khoj vs vLLM", "url": "https://www.anchorterminal.com/compare/khoj-vs-vllm" }, { "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/lemonade-vs-vllm.json", "title": "Lemonade vs vLLM", "url": "https://www.anchorterminal.com/compare/lemonade-vs-vllm" }, { "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/lm-studio-vs-vllm.json", "title": "LM Studio vs vLLM", "url": "https://www.anchorterminal.com/compare/lm-studio-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/localai-vs-vllm.json", "title": "LocalAI vs vLLM", "url": "https://www.anchorterminal.com/compare/localai-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/mlx-lm-vs-vllm.json", "title": "MLX LM vs vLLM", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/ollama-vs-vllm.json", "title": "Ollama vs vLLM", "url": "https://www.anchorterminal.com/compare/ollama-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/open-webui-vs-vllm.json", "title": "Open WebUI vs vLLM", "url": "https://www.anchorterminal.com/compare/open-webui-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/screenpipe-vs-vllm.json", "title": "screenpipe vs vLLM", "url": "https://www.anchorterminal.com/compare/screenpipe-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/text-generation-webui-vs-vllm.json", "title": "TextGen vs vLLM", "url": "https://www.anchorterminal.com/compare/text-generation-webui-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/underdog-vs-vllm.json", "title": "Underdog vs vLLM", "url": "https://www.anchorterminal.com/compare/underdog-vs-vllm" } ], "scores": [ { "by": 3, "edge": "vllm", "hyperqwen": 59, "key": "reliability", "name": "Reliability", "vllm": 62, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 12, "edge": "vllm", "hyperqwen": 56, "key": "schema", "name": "Schema \u0026 documentation", "vllm": 68, "weight": 13 }, { "by": 1, "edge": "hyperqwen", "hyperqwen": 65, "key": "ergonomics", "name": "Agent ergonomics", "vllm": 64, "weight": 13 }, { "by": 9, "edge": "vllm", "hyperqwen": 41, "key": "security", "name": "Security \u0026 auth", "vllm": 50, "weight": 14 }, { "by": 0, "edge": "", "hyperqwen": 60, "key": "payments", "name": "Payments \u0026 pricing", "vllm": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 20, "edge": "vllm", "hyperqwen": 68, "key": "maintenance", "name": "Maintenance \u0026 community", "vllm": 88, "weight": 7 }, { "by": 16, "edge": "vllm", "hyperqwen": 55, "key": "transparency", "name": "Transparency \u0026 trust", "vllm": 71, "weight": 7 } ], "summary": "vLLM scores 58 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 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.", "vllm": "Apache-2.0 software with a release about every two weeks, each with notes that list breaking changes and security fixes. The optional API key covers only some path prefixes, so `/invocations` and control routes such as `/pause` answer without it, and at least 81 security advisories were published in the 12 months to 9 October 2026." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hyperqwen-vs-vllm", "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.min.md" }, "markdown": "vLLM scores 58 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 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- vLLM: grade C, 58/100, rank #695 of 1239. Markdown https://www.anchorterminal.com/tools/vllm.md · JSON https://www.anchorterminal.com/api/v1/tools/vllm.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\nAlso in its favour:\n- No incidents deducted, where vLLM loses 6 points for them\n\nWatch for: Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap\n\n### vLLM (C)\n\nGood for: An owner with a GPU server who wants many concurrent requests against one open-weight model behind OpenAI or Anthropic compatible routes.\n\nAhead on:\n- Schema \u0026 documentation, 68 against 56\n- Security \u0026 auth, 50 against 41\n- Maintenance \u0026 community, 88 against 68\n- Transparency \u0026 trust, 71 against 55\n\nWatch for: `--api-key` guards only the `/v1`, `/v2`, `/inference` and `/cohere` prefixes. `/invocations`, `/pooling`, `/classify`, `/score`, `/rerank`, `/pause` and `/update_weights` answer without it\n\n\n## Score by category\n\n| Category | Weight | HyperQwen | vLLM | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 59 | 62 | vLLM +3 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 56 | 68 | vLLM +12 |\n| Agent ergonomics | 13% (16.2 this run) | 65 | 64 | HyperQwen +1 |\n| Security \u0026 auth | 14% (17.5 this run) | 41 | 50 | vLLM +9 |\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 | 88 | vLLM +20 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 55 | 71 | vLLM +16 |\n| Negative events | ≤15 | 0 | -6 | |\n| **Total** | | **56.9 · C** | **58 · C** | |\n\n## Facts side by side\n\n| Fact | HyperQwen | vLLM |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | syv.ai (syv-ai on GitHub) | vLLM project (PyTorch Foundation) |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | none | 2026-10-02 |\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 | 93k stars |\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**vLLM.** Apache-2.0 software with a release about every two weeks, each with notes that list breaking changes and security fixes. The optional API key covers only some path prefixes, so `/invocations` and control routes such as `/pause` answer without it, and at least 81 security advisories were published in the 12 months to 9 October 2026.\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### vLLM\n\n1. Put a reverse proxy that allowlists routes in front of the server. `--api-key` leaves `/invocations` and the control routes open\n2. Pass `--host 127.0.0.1` for single-machine use. With no `--host` the server listens on every interface\n3. Set `VLLM_NO_USAGE_STATS=1` or `DO_NOT_TRACK=1` before starting if nothing should be sent to stats.vllm.ai\n4. Start with `--enable-auto-tool-choice` and the `--tool-call-parser` for the model before sending tools. Tool calling is off without them\n5. Send `max_tokens` on every request, and read the breaking changes section of the release notes before upgrading a minor version\n\n## Questions\n\n### Which is better for AI agents, HyperQwen or vLLM?\n\nvLLM scores 58 (C) on agent readiness against HyperQwen's 56.9 (C), and leads in 5 of 7 scored categories.\n\n### Do HyperQwen and vLLM need an API key?\n\nNeither needs a key.\n\n### Can an agent call HyperQwen and vLLM without installing anything?\n\nNo hosted endpoint is listed for HyperQwen. No hosted endpoint is listed for vLLM.\n\n### Are HyperQwen and vLLM open source?\n\nYes. HyperQwen is open source (Apache-2.0). vLLM is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hyperqwen\", \"b\": \"vllm\"}`. From a terminal: `anchor compare hyperqwen vllm`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hyperqwen.json and https://www.anchorterminal.com/api/v1/tools/vllm.json\n\n## Other comparisons with HyperQwen or vLLM\n\n- [AnythingLLM vs HyperQwen](https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.md)\n- [AnythingLLM vs vLLM](https://www.anchorterminal.com/compare/anythingllm-vs-vllm.md)\n- [Berth vs HyperQwen](https://www.anchorterminal.com/compare/berth-vs-hyperqwen.md)\n- [Berth vs vLLM](https://www.anchorterminal.com/compare/berth-vs-vllm.md)\n- [BitRun vs HyperQwen](https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen.md)\n- [BitRun vs vLLM](https://www.anchorterminal.com/compare/bitrun-vs-vllm.md)\n- [Docker Model Runner vs HyperQwen](https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.md)\n- [Docker Model Runner vs vLLM](https://www.anchorterminal.com/compare/docker-model-runner-vs-vllm.md)\n- [Foundry Local vs HyperQwen](https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen.md)\n- [Foundry Local vs vLLM](https://www.anchorterminal.com/compare/foundry-local-vs-vllm.md)\n- [Core vs HyperQwen](https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen.md)\n- [Core vs vLLM](https://www.anchorterminal.com/compare/ghost-core-vs-vllm.md)\n- [GPT4All vs HyperQwen](https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.md)\n- [GPT4All vs vLLM](https://www.anchorterminal.com/compare/gpt4all-vs-vllm.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 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- [Jan vs vLLM](https://www.anchorterminal.com/compare/jan-vs-vllm.md)\n- [Khoj vs vLLM](https://www.anchorterminal.com/compare/khoj-vs-vllm.md)\n- [KoboldCpp vs vLLM](https://www.anchorterminal.com/compare/koboldcpp-vs-vllm.md)\n- [Lemonade vs vLLM](https://www.anchorterminal.com/compare/lemonade-vs-vllm.md)\n- [llama.cpp vs vLLM](https://www.anchorterminal.com/compare/llama-cpp-vs-vllm.md)\n- [LM Studio vs vLLM](https://www.anchorterminal.com/compare/lm-studio-vs-vllm.md)\n- [LocalAI vs vLLM](https://www.anchorterminal.com/compare/localai-vs-vllm.md)\n- [MLX LM vs vLLM](https://www.anchorterminal.com/compare/mlx-lm-vs-vllm.md)\n- [Ollama vs vLLM](https://www.anchorterminal.com/compare/ollama-vs-vllm.md)\n- [Open WebUI vs vLLM](https://www.anchorterminal.com/compare/open-webui-vs-vllm.md)\n- [screenpipe vs vLLM](https://www.anchorterminal.com/compare/screenpipe-vs-vllm.md)\n- [TextGen vs vLLM](https://www.anchorterminal.com/compare/text-generation-webui-vs-vllm.md)\n- [HyperQwen vs Underdog](https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.md)\n- [Underdog vs vLLM](https://www.anchorterminal.com/compare/underdog-vs-vllm.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 vLLM", "url": "" } ], "description": "vLLM scores 58 (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", "vLLM C 58", "scores" ], "h1": "HyperQwen vs vLLM", "image": "https://www.anchorterminal.com/assets/og/compare-hyperqwen-vs-vllm.png", "path": "/compare/hyperqwen-vs-vllm", "published": "2026-10-01", "section": "tools", "title": "HyperQwen vs vLLM for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-vllm" }, "tokens": { "markdown": 2600, "slim": 480 }, "version": 1 }