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qwen38-exl3-dflash2

Qwen3.8-27B EXL3 (4.00 bpw) + DFlash2 speculative decoding for ExLlamaV3, validated at 262k context on a 24 GB RTX 3090

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GITHUB · launched Sep 15, 2026 · 18 upvotes (top 43%) · 0 views

Niche: large language models · 191 launches, 4 in the last 7 days ▼ -33% · Explore this niche →

Category and tags

  • Horizontal
  • Model & infra
  • Developer
  • AI-native
  • Hobby / open-source project
  • dflash2
  • exl3
  • exllamav3
  • local-llm
  • quantization
  • rtx3090
  • +1 more

Visit website ↗ Source listing ↗

Could you build this?

No Developing EXL3 low-bit quantizations and DFlash speculative decoding kernels for ExLlamaV3 at 262k context requires deep CUDA/C++ kernel engineering and low-level LLM quantization math.

What it would actually take: This requires C++/CUDA expertise modifying ExLlamaV3 kernels to implement custom 4.00 bpw dequantization routines, draft-target speculative decoding synchronization (DFlash2), and paged attention memory management for extreme 262k context windows on consumer 24GB GPUs. Deep understanding of GPU memory hierarchies, tensor core assembly, and LLM inference engine architectures is required.

Competitors

Other products that read as similar to this one : 779 launches clear the similarity bar, closest 8 shown.

Attention rank: #327 of 780 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 320 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.