"Revolutionizing AI Decoding: NVIDIA's Helix Parallelism Enhances Real-Time Inference"
AI applications rely on models with huge parameter counts and multi-million-token context windows. NVIDIA Blackwell systems offer FP4 compute and high-bandwidth NVLink domain to support real-time decoding at scale. Helix Parallelism enables AI agents to serve more people faster by addressing decoding bottlenecks like KV cache streaming and FFN weight loading.
Traditional parallelism strategies struggle to optimize KV cache streaming and FFN weight loading simultaneously. Helix Parallelism introduces a hybrid sharding strategy that disaggregates attention and FFNs into a temporal pipeline, overcoming decoding bottlenecks during multi-million-token decoding. It allows for efficient execution of attention and FFN stages, improving scalability and interactivity.
Helix Parallelism utilizes a sharding strategy that shards the KV cache along the sequence dimension and applies Tensor Parallelism across attention heads. This approach allows for collaboration across multiple GPUs without duplicating KV cache. By implementing fine-grained pipelining techniques like HOP-B, Helix further reduces token-to-token latency, enhancing real-time decoding performance.
After attention computation, the same pool of GPUs is reprovisioned for FFN block execution without idle time. Helix optimizes the post-attention linear projection and FFN computation using different layouts based on the model type. By staggering KV cache updates and balancing memory usage across GPUs, Helix ensures consistent throughput during decoding.
Helix achieves a new performance benchmark for long-context LLM decoding, improving the number of concurrent users by up to 32x and reducing minimum achievable TTL by up to 1.5x. By sharding KV cache and FFN weights, Helix enhances compute efficiency and pushes the throughput-latency frontier. This optimization provides a blueprint for serving multi-million-token models at scale without sacrificing interactivity.
Read more at Nvidia: Asking an Encyclopedia-Sized Question: How To Make the World Smarter with Multi-Million Token Real-Time Inference