This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Waiting for data. Loading
Product comments vs the next 3
Waiting for data. Loading
Product upvote speed vs the next 3
Waiting for data. Loading
Product upvotes and comments
Waiting for data. Loading
Product vs the next 3
Loading
mlx-serve
Local AI on Apple Silicon: LLMs, image/video gen, agents
Native Zig inference server for Apple Silicon — no Python, no conda. One binary. 35%+ faster decode than LM Studio on Gemma 4 E4B 4-bit. Drop-in replacement for Ollama (/api/chat, /api/generate), plus full OpenAI and Anthropic APIs on the same port. Beyond chat: local image gen (FLUX.2 + Krea-2), video gen with audio (LTX-Video), voice cloning (Qwen3-TTS + ECAPA-TDNN), and an agent that runs in an isolated Linux VM on Virtualization.framework. Free macOS menu-bar app included.
Hi Product Hunt! I'm David, the solo developer behind mlx-serve.
I built this because I wanted local AI that just works on Apple Silicon without Python environments, conda, or 10-step setup guides. One binary, brew install, done.
What mlx-serve does:
LLM inference: 35%+ faster decode than LM Studio on Gemma 4 E4B 4-bit
- Drop-in for Ollama (/api/chat, /api/generate) and OpenAI + Anthropic APIs on the same port
- Agent mode with an isolated Linux VM sandbox (Virtualization.framework, no libcontainer)
- Free macOS menu-bar app included
Claude Code, Raycast, Open WebUI, Cursor, and anything that speaks OpenAI or Ollama APIs works out of the box.
The whole server is written in Zig (no Python, no Go, no Node). Happy to answer any questions about the architecture, the performance work, or why on earth I chose Zig for this. AMA!
About mlx-serve on Product Hunt
“Local AI on Apple Silicon: LLMs, image/video gen, agents”
mlx-serve was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #157 on the daily leaderboard. Native Zig inference server for Apple Silicon — no Python, no conda. One binary. 35%+ faster decode than LM Studio on Gemma 4 E4B 4-bit. Drop-in replacement for Ollama (/api/chat, /api/generate), plus full OpenAI and Anthropic APIs on the same port. Beyond chat: local image gen (FLUX.2 + Krea-2), video gen with audio (LTX-Video), voice cloning (Qwen3-TTS + ECAPA-TDNN), and an agent that runs in an isolated Linux VM on Virtualization.framework. Free macOS menu-bar app included.
On the analytics side, mlx-serve competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how mlx-serve performed against the three products that launched closest to it on the same day.
Who hunted mlx-serve?
mlx-serve was hunted by David Dalcu. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of mlx-serve including community comment highlights and product details, visit the product overview.
David Dalcu
Hi Product Hunt! I'm David, the solo developer behind mlx-serve.
I built this because I wanted local AI that just works on Apple Silicon without Python environments, conda, or 10-step setup guides. One binary, brew install, done.
What mlx-serve does:
LLM inference: 35%+ faster decode than LM Studio on Gemma 4 E4B 4-bit
- Drop-in for Ollama (/api/chat, /api/generate) and OpenAI + Anthropic APIs on the same port
- Local image gen: FLUX.2 and Krea-2 Turbo
- Video gen with audio: LTX-Video 2.3
- Voice cloning: Qwen3-TTS + ECAPA-TDNN speaker encoder
- Agent mode with an isolated Linux VM sandbox (Virtualization.framework, no libcontainer)
- Free macOS menu-bar app included
Claude Code, Raycast, Open WebUI, Cursor, and anything that speaks OpenAI or Ollama APIs works out of the box.
The whole server is written in Zig (no Python, no Go, no Node). Happy to answer any questions about the architecture, the performance work, or why on earth I chose Zig for this. AMA!