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FlashQLA
A
Claude Code
S
Luma Dream Machine
A
Perplexity
S
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.Anthropic's CLI agent. Opus-powered, operates on your repo directly.Smooth, cinematic motion. Image-to-video specialist.AI search done right. Cited answers, not chat theater.
CategoryDev PlatformCodingVideoResearch
PricingFree (MIT License, open-source)Part of Claude Pro/Max/Team plansFree + $10-$500/moFree + $20/mo 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.Developers who want an agent, not autocomplete. Large refactors, tests, docs.Photographers animating stills, cinematic b-roll.Replacing Google for any question where you want a cited answer in seconds.
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
  • Runs locally, edits your actual files
  • Strong on large codebases with 1M context
  • Great at multi-step tasks
  • Best image-to-video in the category
  • Great camera motion control
  • Ray 2 model produces striking shots
  • Sources every claim
  • Fast, current answers
  • Pro Search runs multi-step research
  • Spaces for persistent context
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
  • Terminal-based — learning curve
  • Can't be used without Claude subscription
  • Prompt fidelity below Runway
  • Queue times on free tier
  • Not a general chatbot
  • Answers can be shallow on complex topics
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 if you live in the terminal. Different shape than Cursor — complementary, not replacement.A-tier. Best for cinematic image-to-video. Pair with Runway for coverage.S-tier for search. I use it before Google now. If you're still Googling everything, try this for a week.
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