FreeToken vs Ollama
Edge-native MoE serving engine: bandwidth-adaptive CPU-GPU co-execution, global LRU expert caching and elastic VRAM run 290B+ frontier MoE models on a gaming PC at interactive speed. — versus — Run Llama, Mistral and other open models locally with a single command and a clean API.
ollama is the one-command runner for local models that fit and the friendlier daily driver; FreeToken exists precisely for the frontier MoE weights that don't fit.
| FreeToken | Ollama | |
|---|---|---|
| Stars | 4.3k | 179k |
| Forks | 388 | 18k |
| Language | Python | Go |
| License | Apache-2.0 | MIT |
| Last activity | today | yesterday |
| Topics | local | local |
| Curated connections | 8 | 24 |
FreeToken — the curator's take
The research-grade answer to 'frontier MoE on one consumer box' — Berkeley/MIT-flavored author list, an arXiv paper behind it, and the two ideas that matter for agents: a q* policy that splits work across GPU, CPU and host memory by available bandwidth, and semantic anchor checkpoints so tool calls and thinking-block edits don't force full context recompute. Ships Anthropic/OpenAI-compatible APIs, so Claude Code, Codex or OpenCode can point straight at it, plus a desktop GUI. Constraints are real: NVIDIA RTX 30/40/50 on Windows or Linux, MoE architectures only, and a paper-fresh engine that moves fast — pin versions. Serving a dense 8B on a proper GPU? This is the wrong tool.
Ollama — the curator's take
Run Llama, Mistral and other open models locally with a single command and a clean API.