Posts by Hattie Wu
ATOMesh: Unlocking AMD Hardware for Scalable LLM Serving
- 16 June 2026
Large language model serving is moving from single-engine optimization to full-stack distributed inference. Production deployments must handle high concurrency, long-context prefill, latency-sensitive decode, KV cache store pressure, and multi-node GPU utilization at the same time. On AMD Instinct GPUs, the key opportunity is to connect ROCm-native kernels, communication libraries, inference engines, and distributed orchestration into one scalable serving stack.
ATOM: Unlocking Extreme AMD Instinct Inference with Software-Hardware Co-Optimization
- 15 June 2026
As LLM serving enters a phase defined by high concurrency, long-context workloads, sparse MoE activation, and multi-GPU deployment, the challenge is no longer basic functionality but sustaining peak efficiency on AMD GPUs under production-scale load. ATOM (AiTer Optimized Model) is built for that goal, following four core principles: system-level optimization for LLM inference on AMD Instinct™ GPUs, kernel-level acceleration through AITER, distributed inference scaling with MORI, and a rollout-engine path for RL workloads. It builds on earlier ROCm blog coverage of AITER and vLLM-ATOM, moving from kernel and plugin acceleration into the standalone ATOM inference engine. Rather than being a generic framework adapted to the ROCm™ software, ATOM is an execution engine designed with ROCm-first priorities, AITER-native operators, and deep optimization on the inference-critical path. Aligned with the AMD Instinct roadmap from single-node optimization to multi-node scale-out, ATOM evolves its architecture, kernel strategy, and distributed execution model in lockstep with each hardware generation.
vLLM-ATOM: Unlocking Native AMD Performance in the vLLM Ecosystem
- 07 May 2026
This blog walks you through vLLM-ATOM, the AMD-optimized plugin that supercharges vLLM on Instinct GPUs.