CPD: 40% Faster Long-Context LLM Serving

🔑 Enhanced Key Takeaways

• •CPD's three-level KV-cache hierarchy (GPU memory, host DRAM, and cluster-wide distributed cache via RDMA) enables frequently accessed contexts to migrate closer to GPU over time, reducing latency from seconds to hundreds of milliseconds[3]
• •Together AI's infrastructure achieves sub-second to low-second median time-to-first-token even under saturation conditions where baseline systems exceed one second, validated on synthetic coding agent workloads mirroring real AI-assisted development scenarios[2]
• •The architecture directly addresses the economics of long-context serving: 35-40% throughput improvements translate to measurable infrastructure cost savings or increased user capacity on existing hardware for organizations running AI agents and retrieval-augmented generation systems[2]

🛠️ Technical Deep Dive

• •CPD separates prefill and decode phases by cache hit rate, isolating heavy prefills to dedicated pre-prefill nodes[3]
• •KV cache state from cold requests is written to distributed cache; subsequent similar requests fetch this state in bulk at high bandwidth, converting seconds of compute into hundreds of milliseconds of transfer and light recomputation[3]
• •System evaluated on two critical dimensions: latency and throughput scaling under increasing load (TTFT p50/p90 and per-GPU throughput), and effective serving capacity under contention (sustainable QPS before prefill-side saturation)[3]
• •Decode-side parallelism (increasing from 1D to 2D configurations) improves overall throughput and delays saturation for both baseline and CPD configurations[3]
• •Tested on NVIDIA B200 GPUs with synthetic workloads designed to mirror real AI-assisted development scenarios featuring large shared codebase context with multi-turn interactions[2]

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline