Posts by Zhen Wan
SGLang-ATOM: Bring ROCm-Native Acceleration to SGLang Serving
- 08 July 2026
Large language model serving teams often face two competing goals: keeping the flexibility and developer velocity of an ecosystem serving framework, while also reaching strong throughput, latency, and cost efficiency on production accelerators. In this blog, you will explore how SGLang-ATOM bridges these needs for AMD Instinct GPUs by connecting the SGLang serving experience with ATOM’s ROCm-native execution path.
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.