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docs: add PD disaggregation to GLM-5.2 cookbook playground (#29544)

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Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
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zijiexia
2026-07-02 09:57:38 -07:00
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co-authored by Claude Opus 4.8
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@@ -111,6 +111,7 @@ import { Playground } from "/src/snippets/_playground.jsx";
- **Memory.** The FP8 weights are large (MoE total, not active params). Start around `--mem-fraction-static 0.8` on H200 (TP8) and tune up; raise it for the 4-GPU GB300 single-node layout (TP4).
- **DP-Attention + DeepEP** for the balanced/high-throughput strategies spreads attention across data-parallel ranks and routes MoE through DeepEP.
- **BF16 weights need more GPUs.** The full-precision build (`zai-org/GLM-5.2`, ~1.5 TB) does not fit a single 8×H200 / 8×B200 / 4×GB300 node. It fits single-node on **8×B300** (TP8, ~2.1 TB HBM) — **verified**; on the smaller GPUs it needs a **multi-node** layout (e.g. 2×8×H200 or 2×8×B200 at TP16, 2×4×GB300 at TP8), and those **multi-node BF16 recipes are still proposed/inferred** (`verified: false`). FP8 is the recommended deployment. Use the same DSA / MTP / chunked-prefill guidance as FP8. On B300, BF16 low-latency matches FP8 (the sm103 FP8 path is not yet optimized), but FP8 wins at the balanced/high-throughput points.
- **PD Disaggregation (prefill/decode).** GLM-5.2 is a DSA model and runs under prefill/decode disaggregation — toggle the **PD Disagg** card in the [Playground above](#playground) (pick a Prefill/Decode role + transfer backend, then front the roles with `sglang_router.launch_router --pd-disaggregation`). The Mooncake backend **auto-detects the InfiniBand HCA**, so no device flag is needed by default; only add `--disaggregation-ib-device mlx5_0` (your NIC) if auto-detection picks the wrong device or KV transfer fails to connect. On H200 Docker, expose the IB HCAs to the container (`--privileged --ulimit memlock=-1`, or `--device /dev/infiniband:/dev/infiniband --cap-add IPC_LOCK`) — without IB exposure Mooncake silently falls back to TCP.
- **Chunked-prefill size is regime-dependent.** At long input (8K+) the default `--chunked-prefill-size 2048` is too small and leaves the balanced point prefill-bound (queueing dominates TTFT). Raising it to `--chunked-prefill-size 32768` on the balanced recipe gave roughly **+34–78% output throughput and −39–59% TTFT** on 8×H200 and 8×B200 (8K-in / 1K-out) in our testing. It is **neutral for high-throughput** (decode-bound there) — keep the default. `--max-running-requests` tracks KV capacity, not a tuning free-for-all: ~60–90 concurrent 8K+1K FP8 requests fit on a single 8-GPU node, so pin balanced near `--max-running-requests 80` and let high-throughput run wider.
- **AMD GPUs (MI300X / MI325X / MI355X).** FP8 (`zai-org/GLM-5.2-FP8`) runs single-node at `tp=8` on all three. BF16 (`zai-org/GLM-5.2`, ~1.51 TB) only fits single-node on **MI325X** (2 TB HBM) and **MI355X** (2.3 TB); **MI300X** (1.5 TB) cannot hold the BF16 weights plus KV cache on one node, so use FP8 there (or a multi-node BF16 layout once validated). Use the DSA tilelang backend (`--dsa-prefill-backend tilelang --dsa-decode-backend tilelang`) and add `--chunked-prefill-size 131072` plus `--watchdog-timeout 1200` (20 min for weight loading). FP8 uses about half the memory of BF16 (~89 GB/GPU vs ~175 GB/GPU). GLM-5.2 and DeepSeek-V3.2 share the same model structure; for other DSA / HiSparse tips see the [DeepSeek-V3.2 cookbook](../DeepSeek/DeepSeek-V3_2).