Posts by Lingpeng Jin
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.
Practical, Fault‑Robust Distributed Inference for DeepSeek on AMD MI300X
- 12 November 2025
As large scale LLM inference moves beyond a single server, engineering teams face a familiar trifecta of challenges: performance, fault isolation, and operational efficiency. DeepSeek‑V3/R1’s high‑sparsity Mixture‑of‑Experts (MoE) architecture can deliver excellent throughput, but only when computation, memory, and communication are orchestrated with care—especially across multiple nodes [1].
AITER: AI Tensor Engine For ROCm
- 21 March 2025
Performance optimization is critical when working with GPUs, especially for tasks involving artificial intelligence, which can be extremely demanding. To fully leverage the capabilities of advanced hardware, it’s essential to master optimization strategies and ensure every available resource is utilized efficiently. In this blog we will provide an overview of AMD’s AI Tensor Engine for ROCm (AITER) and show you how easy it is to integrate AITER kernels in basic LLM training and inference workload. AITER helps developers to focus on creating operators while allowing customers to seamlessly integrate this operator collection into their own private, public, or any custom framework.