Posts by Aswin Mathews
From Operator to Kernel: Profile DeepSeek-V4-Flash in Primus with madengine
- 05 November 2026
A slow Primus training step can originate in framework operators, the GPU kernels those operators launch, communication, or the gaps between them. This post follows an eight-layer training proxy derived from the DeepSeek-V4-Flash Primus configuration across a controlled series of madengine runs to distinguish among those sources and identify the next measurable optimization.
DI Series: Serving Kimi-K3 MXFP4 Wide-EP Disaggregated on AMD Instinct MI300X and MI325X
- 08 October 2026
This post is part of the AMD Disaggregated Inference (DI) Blogs Series, which takes frontier open models from a single-node demo to production, multi-node serving on AMD Instinct™ GPUs
Benchmarking Kimi-K3 Across vLLM, SGLang, and ATOM on MI350X
- 22 September 2026
Moonshot AI released the weights for Kimi-K3, a 2.8-trillion-parameter, 1M-context, natively-MXFP4 Mixture-of-Experts (MoE) model, and AMD Instinct™ support was available on day 0 across three independent serving frameworks: vLLM, SGLang, and ATOM. The recipes target the gfx950 generation (MI350X and MI355X). The measurements in this post were taken on an 8× MI350X node.
DI Series: Scaling GLM-5.1-FP8 to 64 MI300X GPUs
- 21 August 2026
Serving a frontier Mixture-of-Experts (MoE) model well is a systems problem, and it gets harder the moment one node is not enough. GLM-5.1 is a good example: it is a large, sparse MoE that users want to run at long context, and it ships a new attention family that breaks assumptions older serving stacks quietly relied on. Fitting it on eight GPUs is only the start. The real question is how to keep it correct and fast as you spread it across several nodes.