Vmlx

If you run local LLMs on a Mac, vMLX is the performance leader. Its prefix caching with multi-context support is a genuine differentiator — switching conversations doesn't evict the cache, which dramatically speeds up iterative workflows. The paged KV cache and continuous batching for up to 256 sequences are features you won't find in LM Studio or Ollama at any price, and they matter for agentic or batch tasks. That said, vMLX is macOS-only — no Windows or Linux support. It also requires Apple Silicon; Intel Macs won't work. The tool is technical: you'll want comfort with command lines and configuring inference parameters. For casual users who just want a simple chatbot interface, alternatives like LM Studio or Ollama are easier. Compared to local inference engines, vLLM-MLX is already included (one-click install), so you skip manual setup. But model quality depends on what you download from HuggingFace — vMLX doesn't curate models. In practice, we'd reach for vMLX when we need to run multiple conversations concurrently, reuse large system prompts across sessions, or serve local API endpoints with low latency. For a single-user chat, the edge narrows. Where it bites: no model fine-tuning or training; purely inference. No web UI beyond the local OpenAI-compatible API; the built-in chat UI is functional but basic. And while it's free, you pay in hardware — larger models need a lot of Mac memory. Bottom line: vMLX is the most performant free option for Apple Silicon inference, best for developers and power users who push local models hard.