LLM serving splits into two phases to double GPU throughput

Modern LLM serving architectures are evolving to handle requests more efficiently by splitting the process into two distinct phases: prefill and decode. The prefill phase, which processes the entire prompt, is compute-bound and benefits from high GPU utilization. The decode phase, responsible for generating tokens one by one, is memory-bandwidth-bound and requires efficient KV cache management. Innovations like PagedAttention, continuous batching, and chunked prefill are crucial for optimizing these phases, with disaggregated serving becoming the industry standard to maximize throughput and minimize latency. AI

IMPACT Optimized LLM serving architectures are crucial for reducing inference costs and improving response times, enabling wider adoption of AI applications.

RANK_REASON The item details technical optimizations and architectural shifts in LLM serving infrastructure. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• chunked prefill
• Continuous Batching
• graphics processing unit
• KV cache
• NVIDIA Dynamo
• Orca
• PagedAttention
• Prefix Caching
• RadixAttention
• Sarathi-Serve
• vLLM

• infra

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