FlashPrefillV2 gives 47 long‑context speedup
This story is from 2026-08-26. It is preserved in the archive; the latest stories are on the live feed.
Sparse block‑prefill kernels now make 128 K token prompts tractable for dense LLMs, shattering the long‑standing quadratic bottleneck that forces most services to truncate inputs. FlashPrefill V2 flips the script by delivering an order‑of‑magnitude speedup while keeping output quality essentially i…
Read the full story at DEV Community — Machine Learning ↗
Timeline · 1 report
- 2026-08-26 05:00 · DEV Community — Machine Learning
FlashPrefillV2 gives 47 long‑context speedup
More stories
- Introducing Kimi K3 on Amazon Bedrock — AWS Machine Learning Blog
- Introducing Amazon SageMaker HyperPod Inference Gateway — AWS Machine Learning Blog
- Anthropic, OpenAI, SpaceXAI, Google sued over call to ‘pace’ AI development — Politico Technology
- Gemini Hacked Three Companies in First Known Breakout by Google’s AI — Wall Street Journal Technology
- Alibaba ships Qwen3.8-Omni-Flash to watch, listen and call tools — r/LocalLLM
- NVIDIA CEO Jensen Huang rejects ‘AI will end the world’ claim, yet cautions ‘we should go as fast as we can but...’ — Mint AI
- Meet the Data Agent in ChatGPT Work — OpenAI YouTube
- Qwen Image 2.1 PR to ComfyUI — r/StableDiffusion
Get the daily brief of stories like this at 6:30 every morning →