FireAttention V3: Enabling AMD as a Viable Alternative for GPU Inference

FireAttention V3 introduces an AMD-specific implementation for Fireworks LLM, showcasing the AMD MI300 GPU as a competitive alternative to NVIDIA's H100 for large language model (LLM) inference. Benchmarks reveal that FireAttention V3 achieves significant performance improvements, with a 1.4x increase in average requests per second (RPS) for the LLaMA 8B model and up to 5.5x in low-latency scenarios compared to AMD's vLLM. The transition to AMD's ROCm platform was smoother than anticipated, leveraging PyTorch's matured support. However, achieving optimal performance required addressing specific LLM-related challenges, particularly in kernel optimization and memory management. The MI300's architecture, while offering higher memory bandwidth, presents limitations in floating-point operations compared to NVIDIA's offerings. Fireworks LLM outperformed both NIM Containers and AMD vLLM in various benchmarks, particularly in low-latency scenarios. The analysis concludes that while AMD's MI300 provides a viable option for GPU inference, further optimizations are necessary for memory-intensive and compute-bound applications.

- FireAttention V3 enhances AMD MI300 GPU performance for LLM inference.

- Significant improvements in RPS and latency metrics were observed compared to NVIDIA's H100.

- Transition to AMD's ROCm was facilitated by PyTorch's support, but performance optimization required additional efforts.

- Fireworks LLM demonstrated superior performance in benchmarks against NIM Containers and AMD vLLM.

- Further optimizations are needed for AMD hardware to compete in memory-bound and compute-heavy applications.