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bonsai2-small-gpu

run ternary bonsai 2 27b well on the gpus people own: serve lines per vram tier, a 1.5x decode kernel for the prismml fork, the qwen 3.8 mtp head grafted back, sweeps by pr

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GITHUB · launched Sep 19, 2026 · 54 upvotes (top 16%) · 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 Writing custom GPU decode kernels (CUDA/Triton) and grafting multi-token prediction heads for quantized 27B ternary models requires deep low-level ML systems and compiler engineering.

What it would actually take: This project requires custom CUDA/Triton decode kernels optimized for ternary weight representations, memory hierarchy tuning across VRAM tiers, and surgically modifying model architectures (grafting Qwen 3.8 MTP heads). The stack involves PyTorch C++ extensions, CUDA, FlashAttention internals, and custom GEMM/GEMV implementations. Success requires a specialized GPU systems engineer with deep expertise in LLM inference engines and hardware-level performance profiling.

Competitors

Other products that read as similar to this one : 300 launches clear the similarity bar, closest 8 shown.

Attention rank: #48 of 301 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 319 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.