Home Resources White Papers Faster LLM Inference on AMD Requires Rethinking All-Reduce

Faster LLM Inference on AMD Requires Rethinking All-Reduce

Extracting maximum ROI from AMD AI clusters requires moving beyond out-of-the-box software bottlenecks.

While AMD Instinct GPUs offer massive compute density, standard all-reduce communication patterns leave GPUs waiting during tensor parallelism. Explore how DriveNets’ deep-stack software optimization bypasses standard RCCL limitations to lower latency and accelerate Time to First Token (TTFT) under high concurrency.

Faster LLM Inference on AMD Requires Rethinking All-Reduce

This white paper details DriveNets’ hierarchical all-reduce optimization tailored for AMD Instinct GPUs (specifically the MI355X). Learn how tuning the communication stack transforms LLM inference performance:

How traditional ring and tree all-reduce topologies introduce synchronization delays during tensor parallelism
DriveNets’ custom hierarchical kernel that keeps data movement local and enables simultaneous cross-node exchange
Real-world benchmark results, demonstrating up to 4.3x lower TTFT latency and consistent speedups across common message sizes