llmfit vs unlimited-ocr
Two of the top projects in Open models, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
llmfit
Rust tool that checks which LLMs fit your RAM, CPU and GPU
unlimited-ocr
OCR model that parses long documents and multi-page PDFs in one pass
Verdict
llmfit ranks higher, 83 to 55. Of the 3 score parts we measure for both, llmfit leads on 2, unlimited-ocr on none and 1 is even.
- Adoptionllmfit leads by 46 points, 74 to 28: a wide gap.
- FreshnessEven at 100.
- MaintenanceWe do not score unlimited-ocr on this; llmfit has 92.
- Agent-readyllmfit leads by 30 points, 30 to 0: a wide gap.
Pick llmfit if you are
Self-hosters choosing which local LLMs and quantizations their hardware can run
- Detects NVIDIA, AMD, Intel and Apple Silicon hardware, including multi-GPU and unified memory
- Handles MoE models, so memory estimates reflect active parameters rather than total size
Pick unlimited-ocr if you are
Engineers self-hosting OCR for scanned documents and PDFs on NVIDIA GPUs
- Multi-page parsing in one request, with 32768 max context length in the examples
- Three inference paths: Transformers, vLLM (Docker images), and SGLang with OpenAI-compatible API
| What we compare | llmfit | unlimited-ocr |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 74, popular | 28, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 92, healthy | Not scored for this kind |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 37.9k | 27.3k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | None published |
| Language | Rust | Python |
| Docker | Yes | No |
| GPU | Optional | Required |
| arm64 or Apple Silicon | Mentioned | Not stated |
llmfit
llmfit detects CPU cores, RAM, GPU and VRAM (NVIDIA CUDA, AMD ROCm, Intel OneAPI, Apple Silicon), then scores a model catalog on fit, speed, quality and context across quantizations such as GGUF, AWQ, GPTQ and EXL2. It runs as a TUI, a CLI with JSON output, or an HTTP API and web dashboard via `llmfit serve`. Speed figures are estimates from a memory-bandwidth model, and `llmfit bench` can measure real tok/s against a running provider.
Who it is for: Self-hosters choosing which local LLMs and quantizations their hardware can run
Strengths
- Detects NVIDIA, AMD, Intel and Apple Silicon hardware, including multi-GPU and unified memory
- Handles MoE models, so memory estimates reflect active parameters rather than total size
- JSON CLI output and REST endpoints (/api/v1/system, /api/v1/models) suit scripts and pipelines
- Installs via Scoop, Homebrew, MacPorts, uv/pip, cargo or a multi-arch Docker image
Weaknesses
- Speed and fit numbers are estimates unless measured on your hardware or matched to community results
- Does not serve models itself; it recommends and benchmarks against Ollama, llama.cpp, MLX and similar
- Windows binaries may ship unsigned if the signing job fails
- Model catalog is built in, so new models need a release or a custom-model entry
- GPU optional
- Docker
- Needs Ollama, llama.cpp, MLX, Docker Model Runner or LM Studio (optional, for downloads and benchmarks)
- Models: GGUF, AWQ, GPTQ, EXL2
- port 8787
unlimited-ocr
Unlimited-OCR is a Baidu document-parsing vision-language model, building on DeepSeek-OCR, with weights on Hugging Face. The repo ships inference code for Transformers, vLLM and SGLang, the last exposing an OpenAI-compatible chat API. It handles single images and multi-page input, with PDFs converted to images first.
Who it is for: Engineers self-hosting OCR for scanned documents and PDFs on NVIDIA GPUs
Strengths
- Multi-page parsing in one request, with 32768 max context length in the examples
- Three inference paths: Transformers, vLLM (Docker images), and SGLang with OpenAI-compatible API
- infer.py batch script launches SGLang and sends concurrent requests for image dirs or PDFs
- MIT license; training supported through ms-swift
Weaknesses
- Requires an NVIDIA GPU; examples tested only on CUDA 12.9 with Python 3.12
- PDFs must be rasterized to images with PyMuPDF before parsing
- SGLang setup needs a bundled dev wheel and custom logit processor flags
- VRAM requirement is not stated in the README
- GPU required
- Needs PyTorch 2.10.0, transformers 4.57.1, PyMuPDF, vLLM or SGLang (optional)
- Models: baidu/Unlimited-OCR (safetensors)
- port 10000
More in Open models
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.