ruview vs llmfit
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.
ruview
WiFi CSI sensing on ESP32 for presence, breathing, heart rate and pose
llmfit
Rust tool that checks which LLMs fit your RAM, CPU and GPU
Verdict
ruview ranks higher, 95 to 87. Of the 4 score parts we measure for both, ruview leads on 2, llmfit on 1 and 1 is even.
- Adoptionruview leads by 9 points, 98 to 89.
- FreshnessEven at 100.
- Maintenancellmfit leads by 5 points, 93 to 88.
- Agent-readyruview leads by 55 points, 85 to 30: a wide gap.
Pick ruview if you are
Self-hosters and researchers needing camera-free presence and vitals sensing
- Runs on roughly $9 ESP32 nodes; the pretrained encoder fits in 8 KB at 4-bit
- Publishes 21 Home Assistant MQTT entities per node, plus Matter and Apple Home bridges
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
| What we compare | ruview | llmfit |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 98, widely used | 89, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 88, healthy | 93, healthy |
| Agent-ready | 85, ready | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 101.9k | 38.5k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Rust | Rust |
| Docker | Yes | Yes |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Mentioned |
ruview
RuView reads Channel State Information from ESP32-S3/C6 nodes and derives presence, person count, breathing (6–30 BPM), heart rate (40–120 BPM), falls and 17-keypoint pose without cameras. It is written in Rust, with pretrained models on Hugging Face, Home Assistant, Matter and HomeKit integration, and an npm MCP toolkit for AI-guided setup. Several accuracy figures are self-reported, and the README says the on-device pose model is a first cut.
Who it is for: Self-hosters and researchers needing camera-free presence and vitals sensing
Strengths
- Runs on roughly $9 ESP32 nodes; the pretrained encoder fits in 8 KB at 4-bit
- Publishes 21 Home Assistant MQTT entities per node, plus Matter and Apple Home bridges
- Pretrained weights on Hugging Face, plus a phase-variance presence fallback needing no model
- README retracts an earlier accuracy claim and flags unvalidated vitals and synthetic RF accuracy
Weaknesses
- Live sensing needs ESP32-S3/C6 CSI firmware; the Docker image runs simulated data
- Committed on-device pose model scores PCK@20 of 3.0% and its runtime path is a stub
- Through-wall range is about 5 m and signal-dependent
- Very large README and feature surface; hard to tell which parts are production-ready
- GPU optional
- Docker + Compose
- Needs ESP32-S3 or ESP32-C6 boards, Cognitum Seed (optional), Home Assistant / MQTT (optional)
- Models: safetensors, RVF, Candle
- port 3000
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
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