Today for AI
HOT RADAR
computeHEAT 11.9°

AI CURATED HOT STORY · 10/23/2026

GLM-5.3 Optimization: PCP, DCP, and HiSparse Enable 1M Contexts on H200s

1 reports archived1 independent sourcesupdated 10/23/2026, 08:00:00
Synthesis & Latest Updates
1 Sources Cross-Validated

To address memory and prefill bottlenecks when serving long-context agentic workloads with GLM-5.3 on H200 clusters, the team introduced a disaggregated architecture using Prefill Context Parallelism (PCP) and Decode Context Parallelism (DCP). Combined with Hybrid HiSparse to offload KV cache to host memory, this approach achieves 1.7–3.2x faster per-user inference at equal throughput and successfully supports 1-million-token contexts.

LATEST/To address memory and prefill bottlenecks when serving long-context agentic workloads with GLM-5.3 on H200 clusters, the team introduced a disaggregated architecture using Prefill Context Parallelism (PCP) and Decode Context Parallelism (DCP). Combined with Hybrid HiSparse to offload KV cache to host memory, this approach achieves 1.7–3.2x faster per-user inference at equal throughput and successfully supports 1-million-token contexts.

HEAT TRENDHourly heat curve

3 fully observed hours
Now
11.9
Peak
12.604:00
24h change
–

The line compares only sources observed throughout; gap hours are interpolated to keep the trend continuous.

TIMELINECoverage timeline

Total 1 reports · Latest first
  1. vLLM 官方博客T1·78 pts
    • PCP splits prompts by token rather than head, better suiting GLM-5.3's sparse attention than tensor parallelism.
    • DCP combined with Hybrid HiSparse shards and offloads KV cache to host memory, breaking GPU VRAM limits to support 1M contexts.
    • On AgentX benchmarks, the hybrid parallel deployment achieved up to 3.2x faster per-user speed compared to data-parallel setups at equivalent throughput.
GLM-5.3 Optimization: PCP, DCP, and HiSparse Enable 1M Contexts on H200s | Today for AI