Posts by Shiksha Patel
ROCm InferaSim: Cut LLM Serving Trial Costs and Deploy the Best Configuration
- 06 November 2026
Serving an LLM well means making a lot of choices before you know the answer. You pick an engine, a parallel layout, precisions and scheduler settings, and for disaggregated serving a split between prefill and decode. That leaves thousands of candidates, and each trial ties up one or more multi-GPU nodes for minutes. Measuring every candidate in our searches would take about 25,000 GPU-hours, so most teams try a few guesses and keep the best. A guess that misses costs you extra GPUs or a missed latency target.
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.