llmfit vs timesfm
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
timesfm
Pretrained time-series foundation model for zero-shot forecasting from Google Research
Verdict
llmfit ranks higher, 80 to 65. Of the 4 score parts we measure for both, llmfit leads on 2, timesfm on none and 2 are even.
- Adoptionllmfit leads by 16 points, 65 to 49: a wide gap.
- FreshnessEven at 100.
- Maintenancellmfit leads by 34 points, 92 to 58: a wide gap.
- 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 timesfm if you are
Engineers and researchers forecasting time series without per-task training
- Zero-shot forecasting of univariate and multivariate series with past-only and future covariates
- Returns point forecast plus 9 quantiles (0.1 to 0.9) per step
| What we compare | llmfit | timesfm |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 65, popular | 49, known |
| Freshness | 100, active | 100, active |
| Maintenance | 92, healthy | 58, fair |
| 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 | 34.5k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Sep 2026 |
| Last release | Oct 2026 | Aug 2026 |
| Language | Rust | Python |
| Docker | Yes | No |
| 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
timesfm
TimesFM is a pretrained decoder-only model for time-series forecasting, installed as a Python package with PyTorch or MLX (Apple silicon) backends. Version 3.0 forecasts univariate and multivariate series with past-only and past-and-future covariates, returning point forecasts plus nine quantiles (0.1 to 0.9). The code is Apache-2.0, but 3.0 weights carry a non-commercial license.
Who it is for: Engineers and researchers forecasting time series without per-task training
Strengths
- Zero-shot forecasting of univariate and multivariate series with past-only and future covariates
- Returns point forecast plus 9 quantiles (0.1 to 0.9) per step
- MLX backend runs 3.0 on Apple silicon without PyTorch, numerically matched to PyTorch
- Includes LoRA fine-tuning example (HF Transformers + PEFT), unit tests and an agent SKILL.md
Weaknesses
- TimesFM 3.0 weights are non-commercial and non-production; commercial use only via Google Cloud services
- Not an officially supported Google product
- Forecasting only; contexts beyond 15,360 points are truncated to the most recent
- README code imports differ between 3.0 examples and the PyPI 2.0.2 note, which is unclear
- GPU optional
- Needs PyTorch or MLX, Hugging Face checkpoints
- Models: TimesFM 3.0 (PyTorch checkpoint), TimesFM 2.5 (200M params, 16k context), TimesFM 1.0 and 2.0 (v1 archive)
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