Posted in 2026
How to Ensure Kernels Actually Overlap
- 15 February 2026
While the CPU scheduler controls the kernel launch order to favor overlapping, the GPU’s Hyper-Q driver [Bradley, 2013] ultimately dictates the actual execution order. This process is inherently non-deterministic and heavily influenced by transient GPU resource occupancy.
Distributed-Native FFA (Coming Soon)
- 14 February 2026
The upcoming blog post will be released in the near future. Stay tuned!
Attention Engine for Inference (Coming Soon)
- 08 February 2026
The upcoming blog post will be released in the near future. Stay tuned!
Support Blackwell with FFA_FA4 Backend
- 07 February 2026
Before the release of MagiAttention-v1.1.0, MagiAttention had supported only the Hopper GPUs, since the attention kernel backend Flex-Flash-Attention (FFA) is built upon open-sourced Flash-Attention 3 (FA3) [Shah et al., 2024], tailored for SM90 compute capability.
Support Muon QK-Clip
- 04 February 2026
The Muon optimizer [Jordan et al., 2024], which leverages matrix orthogonalization, has shown faster convergence than traditional optimizers such as Adam [Kingma and Ba, 2017, Loshchilov and Hutter, 2019] on smaller language models and was subsequently demonstrated to scale to large models by Kimi [Liu et al., 2025].
Optimize Sparse Attention in FFA (Coming Soon)
- 25 January 2026
The upcoming blog post will be released in the near future. Stay tuned!
Support Native Group Collective
- 24 January 2026
With the release of MagiAttention-v1.1.0, we are excited to announce the support for native group collective CUDA kernels for both intranode and internode communication, based upon the amazing work of DeepEP [Zhao et al., 2025].
Dynamic Attention Solver
- 21 January 2026
Context Parallelism (CP) shards sequence activations across distributed devices to overcome memory constraints in long-context training. In MagiAttention, the static attn solver has largely solved CP scheduling for standard long-context workloads where masks are static or deterministic prior to the iteration (e.g. causal, causal document, sliding window). By leveraging initial token dispatch before the iteration starts, the static solver ensures compute balance across ranks while restricting data movement to \(\mathrm{KV}\)-communication only, keeping Query/Output (\(\mathrm{QO}\)) tokens fixed on their host devices.