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FlashQLA
A
Hugging Face
S
Cursor
S
Sora
A
TaglineQwen's open-source GPU kernel library that squeezes 2–3× more speed out of linear attention on NVIDIA Hopper hardware — if you're lucky enough to own one.The GitHub of AI. Models, datasets, spaces — all in one.VS Code fork that made AI coding actually work.OpenAI's video model. Long clips, cinematic quality.
CategoryDev PlatformDev PlatformCodingVideo
PricingFree (MIT License, open-source)Free + $9-$20/mo + enterpriseFree + $20/mo Pro + $40/mo BusinessIncluded with ChatGPT Plus/Pro
Best forML engineers and researchers running Qwen3.x linear-attention models on H100/H200 clusters who need to close the gap between theoretical GDN efficiency and actual hardware throughput.Any ML/AI developer. Hobbyists exploring open models.Developers. Non-developers who want to ship working code.ChatGPT subscribers experimenting with cinematic shots.
Strengths
  • 2–3× forward-pass and ~2× backward-pass speedup over FLA Triton kernels on Hopper GPUs
  • Gate-driven automatic intra-card context parallelism boosts SM utilization in long-sequence, small-head-count regimes without manual config
  • Hardware-friendly algebraic reformulation reduces Tensor Core, CUDA Core, and SFU overhead with no numerical precision loss
  • MIT licensed and fully open-source — drop it straight into Qwen3.x training and inference pipelines
  • Largest open-source AI model hub
  • Hosted inference via Spaces + Inference Endpoints
  • Great community
  • Tab completion feels like mind-reading
  • Composer for multi-file edits
  • Runs Claude, GPT, Gemini — you pick
  • Up to 20-sec clips at 1080p
  • Strong physics + scene composition
  • Storyboard feature for longer narratives
  • Remix existing videos
Weaknesses
  • Extremely narrow hardware requirement: SM90+ only (H100/H200, DGX Spark) with CUDA 12.8+ and PyTorch 2.8+ — useless outside Hopper-class clusters
  • GDN/Qwen-specific: not a drop-in replacement for FlashAttention-style softmax kernels, and won't help you if you're not running linear-attention Qwen models
  • Very new, minimal community adoption or third-party validation yet
  • Overwhelming for beginners
  • Hosted inference pricing varies
  • Can feel overwhelming for non-coders
  • Expensive at scale
  • Stricter content policy than competitors
  • Hit-or-miss on complex motion
  • Text-in-video still struggles
Kai's verdictA genuinely impressive, laser-focused kernel optimization from the Qwen team — real speedups on real hardware — but its utility is gated behind Hopper GPUs and Qwen's GDN architecture, making it a niche power tool rather than a broadly useful library. (Verdict pending Phi's full review.)S-tier infrastructure. The one platform every AI dev eventually uses.S-tier for coding. If you write code of any kind, this pays back the $20 in a day.A-tier. Amazing when it works, frustrating when it doesn't. Runway still more reliable for pros.
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