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Rapid-MLX

Run local LLMs on Mac, 2-3x faster than alternatives

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HN · launched Apr 18, 2026 · 9 upvotes (top 45%) · 0 views

Niche: llm inference engines · 133 launches, 4 in the last 7 days ▼ -33% · Explore this niche →

Category and tags

  • Horizontal
  • Model & infra
  • Developer
  • AI-native
  • Hobby / open-source project

Visit website ↗ Source listing ↗

Could you build this?

No Beating existing optimized inference engines (like Apple MLX or llama.cpp) by 2-3x requires expert-level Metal Shading Language (MSL) kernel programming, GPU memory layout optimization, and hardware-level quantization schemes.

What it would actually take: The stack requires Apple Silicon MLX, C++, and custom Metal compute kernels. The hard parts involve low-latency attention mechanisms (e.g., FlashAttention customized for Unified Memory Architecture), kernel fusion for matrix multiplication and activations, and fine-tuned KV-cache paging. This requires deep GPU systems programming expertise and low-level Apple silicon architecture tuning.

Discussion

4 comments analyzed.

Competitors mentioned: Ollama, omlx, fast-mlx, existing inference servers

Concerns raised: Most models fail at structured tool calling, Existing servers are slow on MLX, Non-Qwen models have inconsistent tool calling (40-100% depending on framework)

Feature requests: Benchmarks against omlx and fast-mlx, Support for other open source projects beyond coding

Competitors

Other products that read as similar to this one : 1621 launches clear the similarity bar, closest 8 shown.

Attention rank: #736 of 1622 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 171 days after the earliest competitor.

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