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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

Details

External ID
1377373768
Source
GITHUB
Company
—
Product
bonsai2-small-gpu
Website domain
github.com
Launched
Sept. 19, 2026
Cohort
—
Upvotes
54
Upvotes percentile
0.8381373302587753
Tags
—
Fetched at
Sept. 23, 2026, 5:02 p.m.
Updated at
Sept. 23, 2026, 5:02 p.m.

Enrichment

Theme
gpu compute and acceleration tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
inference optimization for bonsai 2 on consumer gpus
Manually corrected
False

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 — 136 launches clear the similarity bar, closest 8 shown.

Attention rank: #23 of 137 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 305 days after the earliest competitor.

Other launches for this product

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