Posts by Yixing Xu
Gumiho: A New Paradigm for Speculative Decoding — Earlier Tokens in a Draft Sequence Matter More
- 14 October 2025
Speculative decoding has emerged as a promising approach to accelerate large language model (LLM) inference, yet existing methods face a tradeoff: parallel designs achieve higher speed but lose accuracy, while serial designs gain accuracy at the cost of efficiency. In our recent paper Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding, we introduce a new paradigm that addresses this bottleneck by prioritizing accuracy on the earliest draft tokens, which matters most for downstream acceptance. In this blog, we will discuss the motivation behind Gumiho, the theoretical foundation showing why early-token accuracy dominates, and the novel hybrid architecture that combines serial and parallel decoding to realize these insights. Our goal is to demonstrate both the scientific contributions and practical benefits of Gumiho, showing how it delivers state-of-the-art performance on AMD GPUs using the ROCm software stack, ensuring that the method is widely accessible and optimized for real-world deployment.
Technical Dive into AMD’s MLPerf Inference v5.1 Submission
- 09 September 2025
In the rapidly evolving landscape of artificial intelligence, the demand for reliable and efficient model inference has never been greater. With advancements in large language models (LLMs) and a growing reliance on real-time applications, benchmarks are critical in evaluating how well AI systems perform under varying conditions. Enter MLPerf Inference: Datacenter v5.1 — a significant update to the well-respected benchmarking suite that assesses inference performance across a wide array of models and use cases, catering especially to data centers.
Slim Down Your Llama: Pruning & Fine-Tuning for Maximum Performance
- 09 September 2025
In this blog, we demonstrate how quantization, intelligent depth pruning and supervised fine-tuning can dramatically improve the inference performance of Meta’s Llama 3.1 405B model on AMD Instinct MI355X GPUs. By applying quantization and reducing the number of layers from the original 126, we are able to decrease memory requirements and boost token throughput. Additionally, with carefully applied fine-tuning, we maintain high inference accuracy for both RougeL and Exact Match metrics on MLPerf workloads. To see how these optimizations fit into AMD’s broader MLPerf Inference v5.1 efforts, read Reproducing the AMD Instinct™ GPUs MLPerf Inference v5.1 Submission. For a detailed technical breakdown into other optimizations, check out our Technical Dive into AMD’s MLPerf Inference v5.1 Submission.
Reproducing the AMD Instinct™ GPUs MLPerf Inference v5.1 Submission
- 09 September 2025
MLPerf Inference v5.1 marks AMD’s third round of submissions and the most ambitious yet. This round features submissions on AMD Instinct MI325X and MI355X systems, including multi-node inference and models in MXFP4 datatype. Building upon the success in MLPerf Inference v5.0, AMD has submitted improved results for Llama 2 70B and SDXL on the MI325X platform in this round using new optimization techniques. For a deeper look at these optimizations, see our Technical Dive into AMD’s MLPerf Inference v5.1 Submission. Additionally, explore how we optimized Llama 3.1 405B through pruning and fine-tuning in Slim Down Your Llama: Pruning & Fine-Tuning for Maximum Performance. In addition, AMD has made submissions for the following workloads: