FireAttention V3: Enabling AMD as a viable alternative for GPU inference

FireAttention V3 has been developed as an AMD-specific implementation for Fireworks LLM, using AMD MI300 GPUs as an alternative to NVIDIA H100 for large language model (LLM) inference. Through benchmarks comparing performance on 8 MI300 GPUs against other leading LLM implementations, FireAttention V3 demonstrated significant improvements in request per second (RPS) metrics, achieving up to 1.8x improvement for the LLaMA 70B model and up to 3x and 5.5x improvements in certain low-latency scenarios. The porting to AMD was aided by PyTorch’s ROCm support, although achieving optimal performance required addressing specific LLM performance challenges not typically covered by standard HIP porting guides. Hardware differences, such as warp sizes and memory configurations, necessitated distinct design choices for maximizing performance on AMD, and while AMD's memory bandwidth is higher, its performance in flops-heavy operations remains inferior to NVIDIA's. Despite these challenges, FireAttention V3's kernel-level optimizations and benchmarks reveal that AMD's MI300 offers a viable alternative with competitive performance for specific LLM use cases, marking a significant development in the GPU LLM inference market.

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