llmfit vs laya
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
laya
Single-pass multilingual classifier for typed choice, score and yes/no decisions
Verdict
llmfit ranks higher, 80 to 67. Of the 4 score parts we measure for both, llmfit leads on 2, laya on none and 2 are even.
- Adoptionllmfit leads by 42 points, 65 to 23: a wide gap.
- FreshnessEven at 100.
- Maintenancellmfit leads by 1 point, 92 to 91: close to even.
- Agent-readyEven at 30.
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 laya if you are
Engineers needing fast multilingual triage, routing and moderation classifiers
- One forward pass: 33 ms per question, 7.2 ms/question batched, measured on a T4
- No generation, so no output parsing; answers are typed choice, score or probability
| What we compare | llmfit | laya |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 65, popular | 23, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 92, healthy | 91, healthy |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 37.9k | 32.9k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Rust | Python |
| Docker | Yes | Yes |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Mentioned |
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
laya
Laya answers typed questions (choice, score, noul) about a text, email, ticket or JSON state in one forward pass of an encoder model, with no text generation. A Router picks between three checkpoints (ModernBERT-large English, mmBERT-base multilingual for 100+ languages, and a typed-decisions fine-tune). It ships as a Python package with a CLI, optional HTTP and MCP servers, and LangChain, LlamaIndex and CrewAI integrations.
Who it is for: Engineers needing fast multilingual triage, routing and moderation classifiers
Strengths
- One forward pass: 33 ms per question, 7.2 ms/question batched, measured on a T4
- No generation, so no output parsing; answers are typed choice, score or probability
- Router selects English or multilingual checkpoint per request; 100+ languages covered
- Fine-tuning notebooks provided; typed-decisions checkpoint scores 0.766 vs 0.362 for base English
Weaknesses
- Zero-shot accuracy is low on domain tasks; fine-tuning is needed for good results
- Multilingual checkpoint ships with a 1,024-token limit; beyond about 4,000 tokens accuracy varies
- Only classifies fixed question types; cannot generate or explain text
- Checkpoints download from Hugging Face on first use, so offline setups need preparation
- GPU optional
- Docker + Compose
- Needs Hugging Face Hub (checkpoint download), PyTorch, transformers
- Models: ModernBERT-large (421M), mmBERT-base (322M), ONNX Runtime
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