Posts by Kyle Zhao
Implementing a High-Performance Custom Diffusion Attention Kernel with FlyDSL
- 17 September 2026
Readers may be familiar with traditional Transformer models and their attention mechanisms. The traditional autoregressive transformers generate tokens iteratively. Since this feature significantly limits the inference throughput, researchers have begun exploring approaches such as diffusion models that can generate multiple tokens in each iteration.
Enabling DeepSeek-V4-Flash Training on AMD Instinct MI355X GPUs with Primus
- 03 September 2026
DeepSeek-AI released the DeepSeek-V4 series on April 24, 2026: a preview pair of MIT-licensed Mixture-of-Experts models, with DeepSeek-V4-Flash at 284B total parameters (13B activated) and a one-million-token context window. Flash pushes sparse attention further than any open-weight model before it: its 43 transformer layers interleave three different attention types, each layer sits inside a hyper-connection block rather than a plain residual, and every token is routed through 256 experts. Each of those choices breaks an assumption baked into stock Megatron-LM training code.
MoE Training Best Practices on AMD GPUs
- 16 December 2025
This blog covers best practices for training Mixture-of-Experts (MoE) models on AMD Instinct™ MI300/MI355-series[a] GPUs with the ROCm ecosystem. Whether you’re new to MoE distributed architectures or optimizing trillion-parameter models, this guide will help you identify bottlenecks and maximize efficiency on AMD hardware.