Flash Attention Speedup
Makes transformer attention faster and less memory-hungry on NVIDIA GPUs
- Repository
- Orchestra-Research/AI-Research-SKILLs
- Stars
- 14,195
- Licence
- MIT
- Files
- 3 files
What it does
When installed, the agent knows how to switch a transformer model's attention computation to Flash Attention, a memory-efficient method that processes attention in chunks instead of building huge intermediate matrices. The skill walks the agent through checking the PyTorch version, enabling the built-in fast attention path, benchmarking the speedup, and checking that outputs still match the standard version. It also covers the separate flash-attn library for features like multi-query attention, sliding windows and H100 FP8.
Great for
- Engineers training or running transformer models on long sequences that hit GPU memory limits
- Developers who want faster inference on NVIDIA GPUs without changing model accuracy
- Teams profiling attention performance before and after an optimization
Skip it if
- Users without an NVIDIA GPU, since the examples require CUDA and the speedups do not apply to CPU-only setups
- Anyone expecting the quoted 2-4x speedup and 10-20x memory reduction as guaranteed; these are general claims and results depend on hardware and sequence length
- Production systems changed without testing, since the skill itself says to verify accuracy against a baseline
Try asking
- “Make my PyTorch transformer use Flash Attention and check that the outputs still match the original”
- “My training runs out of GPU memory on 4096-token sequences; help me enable an efficient attention backend”
- “Benchmark standard attention against Flash Attention on my GPU and report the speedup”
- “Set up sliding window attention with flash-attn for a long-context model”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 3 original files, the MIT licence and an attribution note.
flash-attention.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip flash-attention.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip flash-attention.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Verify the speedup and accuracy on your own hardware and input sizes before relying on it, since the quoted figures are general claims
- Install flash-attn in a dedicated environment, because it compiles CUDA code during installation and can fail on mismatched toolchains
- Review any pip install or benchmark commands the agent proposes before running them, since they execute on your machine
What's inside
- references/benchmarks.md
- references/transformers-integration.md
- SKILL.md
Redistributed unmodified from Orchestra-Research/AI-Research-SKILLs under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.