FlashInfer: The Flash Attention Alternative That Actually Installs

TL;DR
Installing flash-attn often means long compilation times and CUDA compatibility headaches. flashinfer gives you the same fast attention kernels, but as ≈30 MB wheels that install in < 5 s on most CUDA ≥ 11.8 / PyTorch ≥ 2.4 combos.
No ninja, no Triton, no blood sacrifice.

the pain

# You, 45 minutes ago
pip install flash-attn
# ... why isn't it working ...
pip install flash-attn --build-isolation
error: command '/usr/local/cuda/bin/nvcc' failed with exit status 1

If you've spent the last hour watching pip install flash-attn get stuck on "Building wheel for flash-attn (setup.py)", welcome to the club. Flash-attn's build-from-source dance is the new "it compiles on my laptop."

2 h single-threaded if you forgot ninja.
3–5 min with ninja—if nothing's mismatched.
Or it just dies because your minor CUDA patch version hurt its feelings.

enter flashinfer

flashinfer is different—it has easily installable precompiled wheels for most CUDA/PyTorch combos. And it just... works. 30MB download, done.

# This actually completes in seconds
pip install flashinfer-python

Wheels cover:

CUDA	PyTorch
11.8	2.3, 2.4, 2.5
12.1	2.3, 2.4, 2.5, 2.6
12.4	2.4, 2.5, 2.6

Linux only for now.

why it's faster (for inference)

flashinfer isn't "flash-attn with easier packaging."
It's inference-only and exploits that:

Decode-phase attention is bandwidth-bound → kernels optimized for weight-loading, not gradient-sync.
PageAttention → paged KV cache, zero-copy block table management.
Cascade attention → hierarchical caches for shared prefixes.
Fused RoPE/ALiBi → one kernel instead of three memory passes.

Numbers on H100:

Task	Speed-up vs SOTA
inter-token decode	29–69 % ↓ latency
batched generation	13–17 % ↑ throughput

already in production

TensorRT-LLM kernels now ship via flashinfer.
vLLM, SGLang, MLC-Engine have opt-in backends.
AOT compilation for prod (no JIT surprises).

3-line drop-in

import flashinfer as fi

# q, k, v: [batch, seq, heads, dim]
output = fi.single_prefill_with_kv_cache(
    q, k, v,
    causal=True,
    rotary_mode="k_rpe"      # fused RoPE
)

should you switch?

You	Recommendation
Serving LLMs in prod	Yes, yesterday.
Training models	Stick with flash-attn for now.
Installing flash-attn just for inference	Save yourself - use flashinfer