# KoboldCpp vs vLLM

> KoboldCpp scores 60.5 (C) to vLLM's 57.7 (C) for local inference. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/koboldcpp-vs-vllm
- Markdown: https://www.anchorterminal.com/compare/koboldcpp-vs-vllm.md (~2,550 tokens)
- Slim: https://www.anchorterminal.com/compare/koboldcpp-vs-vllm.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/koboldcpp-vs-vllm.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- Updated: 2026-10-09

KoboldCpp scores 60.5 (C) on agent readiness against vLLM's 57.7 (C), and leads in 1 of 7 scored categories. vLLM leads on security & auth, maintenance & community and transparency & trust. Both do local inference.

- KoboldCpp: grade C, 60.5/100, rank #510 of 950. Markdown https://www.anchorterminal.com/tools/koboldcpp.md · JSON https://www.anchorterminal.com/api/v1/tools/koboldcpp.json
- vLLM: grade C, 57.7/100, rank #600 of 950. Markdown https://www.anchorterminal.com/tools/vllm.md · JSON https://www.anchorterminal.com/api/v1/tools/vllm.json
- Best local AI models and assistants: https://www.anchorterminal.com/best/local-ai/index.md
- All 184 local ai comparisons: https://www.anchorterminal.com/compare/local-ai/index.md

## Which one, for what

### KoboldCpp (C)

Good for: An owner who wants text, image, speech and music models behind one executable with a writing and roleplay interface, and clients that speak the KoboldAI, OpenAI, Ollama or Anthropic formats.

Ahead on:
- Reliability, 68 against 62

Also in its favour:
- No incidents deducted, where vLLM loses 6 points for them

Watch for: With no `--host` the server accepts connections on all routable interfaces, and no password is set by default

### vLLM (C)

Good for: An owner with a GPU server who wants many concurrent requests against one open-weight model behind OpenAI or Anthropic compatible routes.

Ahead on:
- Security & auth, 50 against 38
- Maintenance & community, 88 against 82
- Transparency & trust, 67 against 49

Watch for: `--api-key` guards only the `/v1`, `/v2`, `/inference` and `/cohere` prefixes. `/invocations`, `/pooling`, `/classify`, `/score`, `/rerank`, `/pause` and `/update_weights` answer without it


## Score by category

| Category | Weight | KoboldCpp | vLLM | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 68 | 62 | KoboldCpp +6 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 68 | 68 | even |
| Agent ergonomics | 13% (16.2 this run) | 63 | 64 | vLLM +1 |
| Security & auth | 14% (17.5 this run) | 38 | 50 | vLLM +12 |
| Payments & pricing | 10% (12.5 this run) | 60 | 60 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 82 | 88 | vLLM +6 |
| Transparency & trust | 7% (8.8 this run) | 49 | 67 | vLLM +18 |
| Negative events | ≤15 | 0 | -6 | |
| **Total** | | **60.5 · C** | **57.7 · C** | |

## Facts side by side

| Fact | KoboldCpp | vLLM |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | LostRuins (Concedo) | vLLM project (PyTorch Foundation) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | HTTP | HTTP |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | AGPL-3.0 for KoboldCpp and KoboldAI Lite. The bundled GGML, llama.cpp and stable-diffusion.cpp code stays under MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-09-27 | 2026-10-02 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Popularity | 12k stars | 93k stars |

## Verdicts

**KoboldCpp.** One file runs text, image, speech and music models behind a published OpenAPI 3.0.3 document, with eight releases in 90 days. The server listens on every interface with no password by default, and `--password` leaves the image routes open.

**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.

## Before you call either

### KoboldCpp

1. Start with `--host 127.0.0.1` and `--password`. The default listens on every interface with no key
2. Send the password as `Authorization: Bearer <password>`. It is not read from the query string
3. Treat 503 as both busy and rate limited. The server never sends 429 or `Retry-After`, and the wait in seconds is in `detail.msg`
4. Pass `max_length` or `max_tokens`. The default is 2,048 tokens unless `--defaultgenamt` changes it
5. Send a `genkey` with each generation so `/api/extra/generate/check` and `/api/extra/abort` act on your request and not another caller's

### vLLM

1. Put a reverse proxy that allowlists routes in front of the server. `--api-key` leaves `/invocations` and the control routes open
2. Pass `--host 127.0.0.1` for single-machine use. With no `--host` the server listens on every interface
3. Set `VLLM_NO_USAGE_STATS=1` or `DO_NOT_TRACK=1` before starting if nothing should be sent to stats.vllm.ai
4. Start with `--enable-auto-tool-choice` and the `--tool-call-parser` for the model before sending tools. Tool calling is off without them
5. Send `max_tokens` on every request, and read the breaking changes section of the release notes before upgrading a minor version

## Questions

### Which is better for AI agents, KoboldCpp or vLLM?

KoboldCpp scores 60.5 (C) on agent readiness against vLLM's 57.7 (C), and leads in 1 of 7 scored categories. vLLM leads on security & auth, maintenance & community and transparency & trust.

### Do KoboldCpp and vLLM need an API key?

Neither needs a key.

### Can an agent call KoboldCpp and vLLM without installing anything?

No hosted endpoint is listed for KoboldCpp. No hosted endpoint is listed for vLLM.

### Are KoboldCpp and vLLM open source?

Yes. KoboldCpp is open source (AGPL-3.0 for KoboldCpp and KoboldAI Lite. The bundled GGML, llama.cpp and stable-diffusion.cpp code stays under MIT). vLLM is open source (Apache-2.0).


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/koboldcpp-vs-vllm.json, and with the fewest tokens: https://www.anchorterminal.com/compare/koboldcpp-vs-vllm.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "koboldcpp", "b": "vllm"}`. From a terminal: `anchor compare koboldcpp vllm`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/koboldcpp.json and https://www.anchorterminal.com/api/v1/tools/vllm.json

## Other comparisons with KoboldCpp or vLLM

- [AnythingLLM vs KoboldCpp](https://www.anchorterminal.com/compare/anythingllm-vs-koboldcpp.md)
- [AnythingLLM vs vLLM](https://www.anchorterminal.com/compare/anythingllm-vs-vllm.md)
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