llmfit
What it does
An editorial selection.
TUI that inspects your CPU, RAM, GPU and VRAM and tells you which open models and quantizations will run well. It can also benchmark real tokens per second.
Why it is listed
Answers "what can I run locally?" in one command before you download hundreds of GB.
GitHub describes it as: Hundreds of models & providers. One command to find what runs on your hardware.
Counts were observed on 27/10/2026, 23:15:40 UTC. Observations older than 36 hours are excluded from current movement rankings.
Repository at a glance
- Stars
- 38,424
- Forks
- 2,478
- Open issues
- 35
- Merged PRs
- 658
- Commits, last 30 days
- 84
- Stars, 7 days
- unavailable
- Stars, 30 days
- unavailable
Counts observed on 27/10/2026, 23:15:40 UTC. Growth uses complete dated windows; see Methodology.
History
Project details
Explore and get started
Use the project's own documentation for supported platforms, dependencies and installation steps. The README and release notes are maintained by AlexsJones.
- Read the README and setup instructionsFeatures, prerequisites, examples and documentation links.
- Browse releases and changelogsPublished versions and the changes made between releases.
- Check licence and usage termsMIT is the licence reported by GitHub; read the repository terms before use.
- Read pull requestsProposed changes and development discussions.
- Browse reported issuesKnown problems, feature requests and maintainer responses.
Development context
These are observations of work in the repository. They do not establish quality, security or support commitments.
- Total commits on default branch
- 1,300
- Open pull requests
- 28
- Closed issues, lifetime
- 253
- Median time to close an issue
- 20.1 days
- Median time to merge a pull request
- 4.3 days
Close and merge times use up to 20 most recently updated closed issues or merged pull requests. They omit unresolved work and are context, not a maintenance score. Sample limits.
Explore the same category
Other projects in Local AI Inference, ordered by shared topics and then name. These are discovery links, not equivalence claims.
- jundot/omlx
Mac menu-bar LLM server with continuous batching and a hot RAM / cold SSD KV cache.
- VecSzn/PolyStrata
Run large AI models on your own PC: Qwen3.8-Flash-Next on Strata's engine, and GLM 5.3 Flash, DeepSeek V4 Flash, LFM2.5, Qwen3.6, Ornith 1.5, Gemma 4, Tiel Coder and MiniCPM-V on a second engine written for PolyStrata. One-click install for Windows and Linux, OpenAI/Anthropic API on localhost.
- cobanov/PS5LM
llama.cpp on a jailbroken PS5: run any GGUF model natively on the console, starting with Qwen 3.8
- Indras-Mirror/Xolotl
Xolotl (formerly Strata-DSV4.1): run DeepSeek-V4.1-Flash, all 552B, as a local coding agent on one gaming PC (RTX 4090 + 90 GB RAM) at up to ~27 tok/s, 512K context, October 1017 builds included. Plus GLM-5.3-Flash and Qwen3.8. MoE experts tiered across VRAM, pinned RAM and NVMe; OpenAI + Anthropic API.
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