blinkdl/rwkv-cuda — explained in plain English
Analysis updated 2026-07-17 · repo last pushed 2025-12-10
Speed up RWKV model training by replacing the default depthwise convolution with a custom CUDA kernel up to 100x faster.
Reduce chatbot response latency by using optimized CUDA kernels during RWKV inference on Nvidia hardware.
Compare successive kernel optimization versions using the included raw benchmark numbers.
Cut hours off a full RWKV training run by shaving milliseconds off each forward pass.
| blinkdl/rwkv-cuda | yassa9/dvlt.cu | stablemarkk/hash256_miner | |
|---|---|---|---|
| Stars | 232 | 31 | 20 |
| Language | Cuda | Cuda | Cuda |
| Last pushed | 2025-12-10 | — | — |
| Maintenance | Quiet | — | — |
| Setup difficulty | hard | hard | moderate |
| Complexity | 4/5 | 5/5 | 4/5 |
| Audience | researcher | researcher | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires an Nvidia GPU, CUDA toolkit, and the Ninja build tool to compile kernels.
A set of custom CUDA kernels that speed up RWKV language model operations on Nvidia GPUs, cutting a key computation from 94ms to under 1ms.
Mainly Cuda. The stack also includes CUDA, Python, PyTorch.
Quiet — no commits in 6-12 months (last push 2025-12-10).
Not specified in the explanation.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
double-check against the repo, no cap.