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FlashQLA
A
Replicate
S
ChatGPT Operator
B
DeepSeek
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.Run any open-source AI model with an API call.OpenAI's browser agent. Clicks and types on websites for you.Chinese open-weight powerhouse. Crazy cheap, genuinely smart.
CategoryDev PlatformDev PlatformAgentsChatbots
PricingFree (MIT License, open-source)Pay per second of computeIncluded with ChatGPT Pro $200/moFree web + ultra-cheap API (~$0.14/M input tokens)
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 using open-source models (Flux, SDXL, Whisper, etc).Power users willing to pay $200/mo for a browser bot.Developers + cost-conscious builders. Anyone fine with self-hosting.
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
  • Tens of thousands of models (image, video, audio, LLMs)
  • One-line API for any model
  • Cog framework for custom model deploy
  • Actually uses websites — fills forms, clicks, checks out
  • Built into ChatGPT
  • Good for repetitive web tasks
  • Open weights you can self-host
  • Strong reasoning + math
  • Near-free API pricing
  • DeepSeek-V3 / R1 are serious models
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
  • Cold starts on less-popular models
  • Pricing gets real at scale
  • Slow vs doing it yourself
  • Breaks on complex auth flows
  • $200/mo gate
  • Data goes to servers in China — privacy concerns for business use
  • Chinese policy filters
  • English polish trails Western models
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 for open-source model APIs. The default in this space.B-tier. Still early. Manus is more flexible for less money.S-tier for price/performance. A-tier for consumer use. If you build apps, this is the budget pick.
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