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Qwen3.6-35B-A3B on a 16 GB M1 Pro with SSD-streamed MoE

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HN · launched Jul 17, 2026 · 24 upvotes (top 25%) · 0 views

Niche: llm inference and deployment tools · 188 launches, 6 in the last 7 days · 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 Streaming active mixture-of-experts (MoE) weights from an NVMe SSD into Apple Silicon unified memory at token generation speed requires low-level kernel bypass, Metal/MPS programming, and custom memory-mapping paging kernels.

What it would actually take: Requires low-level C++/Metal implementations using POSIX mmap or direct asynchronous I/O (aio) to stream selectively gated expert tensors from disk into VRAM just-in-time during forward passes. The critical difficulty is hiding high SSD read latency behind attention computation and designing custom weight quantizations optimized for Apple Silicon memory bus bandwidth.

Discussion

4 comments analyzed.

Concerns raised: Performance slower than expected, M1 chip compatibility requirements unclear

Feature requests: Support for cached pre-filled system prompt KV-cache with prefix hash in requests, Remote INFER protocol support

Competitors

Other products that read as similar to this one : 696 launches clear the similarity bar, closest 8 shown.

Attention rank: #161 of 697 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 258 days after the earliest competitor.

The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.