llmfit vs speech
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
speech
PyTorch toolkit for training and deploying ASR, TTS and speech LLM models
Verdict
llmfit ranks higher, 86 to 65. Of the 4 score parts we measure for both, llmfit leads on 2, speech on 1 and 1 is even.
- Adoptionllmfit leads by 52 points, 84 to 32: a wide gap.
- FreshnessEven at 100.
- Maintenancellmfit leads by 35 points, 93 to 58: a wide gap.
- Agent-readyspeech leads by 55 points, 85 to 30: 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 speech if you are
Researchers and PyTorch developers training ASR, TTS and speech LLMs
- Covers ASR, TTS and speech LLMs in one codebase with pre-trained open checkpoints
- Streaming ASR checkpoint with 80ms-1s configurable latency and 40 languages
| What we compare | llmfit | speech |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 84, widely used | 32, known |
| Freshness | 100, active | 100, active |
| Maintenance | 93, healthy | 58, fair |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 38k | 18.6k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Aug 2026 |
| Language | Rust | Python |
| Docker | Yes | Yes |
| 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
speech
NVIDIA NeMo Speech is a Python/PyTorch toolkit for building speech models: automatic speech recognition, text-to-speech and speech LLMs. It ships training code and pre-trained checkpoints such as Parakeet, Canary, Nemotron-Speech-Streaming and MagpieTTS, published on Hugging Face. Install from source with uv, from PyPI as nemo-toolkit, or use the NGC container.
Who it is for: Researchers and PyTorch developers training ASR, TTS and speech LLMs
Strengths
- Covers ASR, TTS and speech LLMs in one codebase with pre-trained open checkpoints
- Streaming ASR checkpoint with 80ms-1s configurable latency and 40 languages
- MagpieTTS v2607 supports 12 languages; Parakeet V3 and Canary V2 cover 25 European languages
- Installs over an existing PyTorch 2.7+ build via pip without replacing it
Weaknesses
- NVIDIA GPU with CUDA required for training; only recommended for inference
- Requires Python 3.12 or newer
- Tested stack is pinned to PyTorch 2.11/2.12 with CUDA 12.9 or 13.2
- Not an inference server; no HTTP API or web UI described in the README
- GPU required
- Docker
- Needs PyTorch 2.7+, CUDA, Python 3.12+
- Models: Parakeet, Canary, Nemotron-Speech-Streaming, MagpieTTS, Canary-Qwen-2.5B
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