[AgentX] offload all Muse Spark Open H100 points to HiCache / [AgentX] Muse Spark Open H100 全部并发点启用 HiCache 卸载

inferencex-e2e/benchmarks/single_node/srt-slurm-recipes/musespark/sglang/h100-fp4-mtp/agentic.yaml

@@ -65,31 +65,24 @@ base:
       AIPERF_REQUIRED_SERVER_METRIC_PREFIX: 'sglang:'
       AGENTIC_WARMUP_GRACE_PERIOD: '3600'
 
-zip_override_tp8:
-  roles:
-    agg:
-      args:
-        # Eager batches above the decode graphs OOM in the MoE with ~1 GB free.
-        max-running-requests: [2, 4, 8, 8, 8]
-        # Each graph slot reserves seven full-context int32 page tables, and
-        # bs=16 graphs leave too little headroom for the vision encoder.
-        cuda-graph-max-bs-decode: [2, 4, 8, 8, 8]
-  benchmark:
-    env:
-      CONC: ['1', '2', '4', '8', '16']
-      KV_OFFLOADING: none
-
 # The GPU KV pool is a few GB per rank, so agentic prefixes evict between turns;
 # HiCache keeps them in host DRAM instead of recomputing them.
 zip_override_tp8_hicache:
   roles:
     agg:
       args:
-        max-running-requests: [4, 8, 8]
-        cuda-graph-max-bs-decode: [4, 8, 8]
+        # Eager batches above the decode graphs OOM in the MoE with ~1 GB free.
+        max-running-requests: [2, 4, 8, 8]
+        # Each graph slot reserves seven full-context int32 page tables, and
+        # bs=16 graphs leave too little headroom for the vision encoder.
+        cuda-graph-max-bs-decode: [2, 4, 8, 8]
         enable-hierarchical-cache: true
         hicache-size: 160
+        # 0.97 left ~1.3 GB free and HiCache's serving buffers OOMed it at c4; 0.95
+        # shrank the GPU KV pool to ~98k tokens, below the trace's ~196k-token
+        # inputs. 0.965 keeps ~1.8 GB free and a ~250k-token pool.
+        mem-fraction-static: 0.965
   benchmark:
     env:
-      CONC: ['2', '4', '8']
+      CONC: ['1', '2', '4', '8']
       KV_OFFLOADING: dram

inferencex-e2e/configs/nvidia-master.yaml

@@ -9740,5 +9740,4 @@ musespark-fp4-h100-sglang-agentic-dflash:
     agentic-coding:
     - dram-utilization: 0.80
       search-space:
-      - { tp: 8, ep: 8, kv-offloading: none, spec-decoding: draft_model, conc-list: [1, 2, 4, 8, 16], srt-recipe: agentic.yaml }
-      - { tp: 8, ep: 8, kv-offloading: dram, kv-offload-backend: { name: hicache }, spec-decoding: draft_model, conc-list: [2, 4, 8], srt-recipe: agentic.yaml }
+      - { tp: 8, ep: 8, kv-offloading: dram, kv-offload-backend: { name: hicache }, spec-decoding: draft_model, conc-list: [1, 2, 4, 8], srt-recipe: agentic.yaml }

inferencex-e2e/perf-changelog.yaml

@@ -10014,6 +10014,15 @@
     - "Keep the batch-8 cap on running requests and decode graphs from the GPU-resident sweep."
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/4181
 
+- config-keys:
+    - musespark-fp4-h100-sglang-agentic-dflash
+  scenario-type:
+    - agentic-coding
+  description:
+    - "Run every point with HiCache host-DRAM KV offload at 160 GB per rank, sweeping concurrency 1, 2, 4 and 8; drop the GPU-resident arm and the concurrency 16 queueing point."
+    - "Keep the batch-8 cap on running requests and decode graphs, and lower mem-fraction-static from 0.97 to 0.965: with HiCache attached, 0.97 ran out of GPU memory in the MoE at concurrency 4, while 0.95 shrank the GPU KV pool to 98k tokens and rejected the trace's ~196k-token inputs."
+  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/4182
+
 - config-keys:
     - dsv41flash-fp4-gb300-dynamo-vllm-agentic-dspark-disagg
   scenario-type: