llmfit vs kronos
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
kronos
Pretrained decoder-only models that forecast financial candlestick (K-line) series
Verdict
llmfit ranks higher, 80 to 47. Of the 4 score parts we measure for both, llmfit leads on 3, kronos on 1.
- Adoptionkronos leads by 16 points, 81 to 65: a wide gap.
- Freshnessllmfit leads by 35 points, 100 to 65: a wide gap.
- Maintenancellmfit leads by 82 points, 92 to 10: a wide gap.
- 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 kronos if you are
Quant researchers and ML engineers forecasting candlestick time series
- Open weights at 4.1M, 24.7M and 102.3M params on Hugging Face
- Few-line predict and predict_batch API returning pandas DataFrames
| What we compare | llmfit | kronos |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 65, popular | 81, widely used |
| Freshness | 100, active | 65, recent |
| Maintenance | 92, healthy | 10, weak |
| 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 | 40.8k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Apr 2026 |
| Last release | Oct 2026 | None published |
| Language | Rust | Python |
| Docker | Yes | No |
| GPU | Optional | Optional |
| 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
kronos
Kronos is a family of decoder-only Transformers pretrained on OHLCV candlestick data from over 45 exchanges. A tokenizer quantizes K-lines into hierarchical discrete tokens, and the model forecasts future bars through a KronosPredictor class with temperature and top-p sampling. Weights for mini, small and base are on Hugging Face, and the repo includes a Qlib-based fine-tuning and backtest pipeline.
Who it is for: Quant researchers and ML engineers forecasting candlestick time series
Strengths
- Open weights at 4.1M, 24.7M and 102.3M params on Hugging Face
- Few-line predict and predict_batch API returning pandas DataFrames
- Includes tokenizer and predictor fine-tuning scripts with a Qlib backtest example
- MIT license; paper accepted at AAAI 2026
Weaknesses
- Kronos-large (499.2M) weights are not released
- Context limited to 512 tokens, except 2048 for Kronos-mini
- Batch prediction requires identical lookback and pred_len across all series
- Fine-tuning example is a demo, not a production trading system; no releases tagged
- GPU optional
- Needs PyTorch, Hugging Face Hub, pyqlib (fine-tuning only), Comet.ml (optional)
- Models: Kronos-mini, Kronos-small, Kronos-base, Hugging Face
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