Posts by Anshu Raina
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.
MoE Training Optimization with Primus
- 21 October 2026
Mixture-of-Experts (MoE) has become a dominant architecture for frontier-scale language models, and our users increasingly train large MoE models on AMD Instinct™ MI355X[a] GPUs. Driven by those workload requirements, we have built a broad set of MoE training optimizations in Primus. This blog follows our earlier post MoE Training Best Practices on AMD GPUs. The earlier post established the foundational optimizations; this one describes what we built on top of them.
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.
Primus Tuning Agent: Closing the Configuration-Search Loop
- 06 July 2026
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TraceLens: Democratizing AI Performance Analysis
- 27 April 2026
Profiling modern AI workloads produces huge traces that are hard to interpret. Framework profilers record thousands of operations, kernels, and communication events, and engineers often end up staring at tools like Perfetto UI doing manual calculations. TraceLens speeds this up: it consumes existing framework traces and turns them into structured summaries and comparisons, allowing you to move on to the actual diagnosis and optimization.
Primus Projection: Estimate Memory and Performance Before You Train
- 24 April 2026
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Primus-Pipeline: A More Flexible and Scalable Pipeline Parallelism Implementation
- 23 February 2026
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