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Rows
A
Ollama
S
FlashQLA
A
NotebookLM
S
TaglineSpreadsheets with AI + live integrations baked in.Run LLMs locally. One-line install, GUI optional.Qwen'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.Google's research notebook. Turns your docs into a podcast.
CategoryDataDev PlatformDev PlatformResearch
PricingFree + $19-$89/user/moFree + open sourceFree (MIT License, open-source)Free
Best forOps teams, marketers, anyone building dashboards from multiple sources.Devs wanting offline/local LLMs for privacy or experimentation.ML 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.Students, researchers, anyone with a stack of PDFs or a topic to learn.
Strengths
  • Pull live data from Stripe, Slack, Google Analytics, etc.
  • AI functions inside cells
  • Modern UX
  • Run Llama, Mistral, Qwen, etc. on your laptop
  • Simple CLI + API
  • Hardware-aware (picks the right quant)
  • 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
  • Upload anything, ask questions, get cited answers
  • Audio Overview turns docs into a 10-min podcast
  • Great for studying
Weaknesses
  • Not a full Excel replacement for heavy users
  • Integrations best on paid tiers
  • Needs beefy laptop for larger models
  • Speed way behind cloud APIs
  • 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
  • Google-only
  • Can be slow on large corpora
Kai's verdictA-tier. The most interesting spreadsheet in years. Great for ops dashboards.S-tier for local inference. If you care about privacy or want to tinker, install this today.A 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 study. The Audio Overview is a killer feature. Try it with three of your favorite PDFs.
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