diff --git a/README.md b/README.md
index 6f11b1c..6480177 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,7 @@
 # sskj — 多平台大模型推理性能基准测试项目
 
+**更新（2026-08-18 18:38:17 CST）**：完成 Kimi-K3 四节点 TP32×EP4 的真实 Prefill MoE backend 验收；24/24 重复均 40/40 成功，FlashInfer MXFP4 在 8K Chunk、C=8/16 下相对 Marlin 提升约 11% Input TPS、降低约 10% TTFT，并确认 8K 优于 16K。实验默认 EP 改为 4，补齐非连续 Prefill 输入与 FlashInfer 官方 SiTU 回归，详见 `experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/README.md`。
+
 **更新（2026-08-18 13:55:30 CST）**：Kimi-K3 四节点 MoE backend 验收入口支持通过环境变量覆盖 EP size 及 backend/chunk/concurrency 子矩阵，用于先执行 TP32×EP4、16K Chunk、16K→1、C=16 的最大压力容量验证，同时保留默认 TP32×EP32 全矩阵。
 
 **更新（2026-08-18 13:03:33 CST）**：Phase 5 候选镜像改为针对 Kimi 基础镜像原生 SGLang 源码应用最小兼容补丁，仅补齐 SM120 FlashInfer MXFP4 backend 分发、权重布局与 SiTU 激活映射，避免整体替换新版 Python 包造成 `sglang-kernel` 版本错配。
diff --git a/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/README.md b/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/README.md
index 9a8a881..badba6d 100644
--- a/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/README.md
+++ b/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/README.md
@@ -574,3 +574,103 @@ phase4_ncu_2026_2_1_ab_gpu6_raw.csv
 ```text
 /Users/hzy/Desktop/infra/.phase3_work/phase4_results/
 ```
+
+## Phase 5 四节点真实 Serving
+
+状态：**完成。** 本阶段在 601-604 共 32 张 RTX PRO 6000D 上验证
+TP32/EP4 Kimi-K3，使用真实 SGLang Scheduler、Chunked Prefill、CUDA
+Graph、四节点 NCCL/RDMA 和 HTTP benchmark。
+
+### 正式矩阵
+
+```text
+ISL=16384, OSL=1, requests=40
+TP32, EP4, DP1
+backend=marlin / flashinfer_mxfp4
+chunked_prefill_size=8192 / 16384
+concurrency=8 / 16
+3 repeats per point
+```
+
+Prefix/Radix cache 已禁用；client 使用 `random-ids` 保证精确输入长度，且
+tokenizer 从本地模型目录读取。24/24 repeat 均完成 40/40 请求，errors 为空。
+
+### 三次重复中位数
+
+| Chunk | C | Backend | Input TPS | TTFT P50 | TTFT P95 |
+|---:|---:|---|---:|---:|---:|
+| 8K | 8 | Marlin | 2935.02 | 43.51 s | 46.22 s |
+| 8K | 8 | FlashInfer | 3257.96 | 39.19 s | 41.62 s |
+| 8K | 16 | Marlin | 2934.90 | 87.09 s | 89.86 s |
+| 8K | 16 | FlashInfer | 3260.14 | 78.39 s | 80.88 s |
+| 16K | 8 | Marlin | 2812.91 | 45.47 s | 45.86 s |
+| 16K | 8 | FlashInfer | 3027.50 | 42.27 s | 42.63 s |
+| 16K | 16 | Marlin | 2812.33 | 90.97 s | 91.36 s |
+| 16K | 16 | FlashInfer | 3027.34 | 84.52 s | 84.85 s |
+
+FlashInfer 在四个场景均胜过 Marlin：8K chunk 的 Input TPS 提升约 11%，
+TTFT P50 降低约 10%；16K chunk 的收益约为 +7.6% / -7.1%。同一后端内，
+8K chunk 也优于 16K。因此本工作负载的推荐组合是：
+
+```text
+EP4 + flashinfer_mxfp4 + chunked_prefill_size=8192
+```
+
+已有 EP32 Marlin/8K/C8 两次结果仅作同口径对照：EP4 相对 EP32 的 Input
+TPS +15.94%，TTFT P50 -13.88%。按要求没有补跑 EP32。
+
+### 生产路径修复
+
+真实服务额外发现并修复：旧 Kimi 镜像缺少通用 `gemm1_beta` 字段、
+Prefill hidden states 可能非连续、benchmark tokenizer 必须固定本地路径。
+这些问题均在正式矩阵前复现，并由最终 24 点结果覆盖。
+
+### 证据
+
+服务器原始目录：
+
+```text
+/data/hzy/sskj/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/results/kimi3-ep4-moe-full-20260818-151349/
+/data/hzy/kimi3-ep4-moe-full-20260818-151349.log
+```
+
+仓库内汇总：
+
+```text
+results/phase5_ep4_moe_backend_prefill_summary.json
+results/phase5_ep4_moe_backend_prefill_results.csv
+```
+
+日志扫描未发现 OOM、Traceback、NCCL error 或 EngineDeadError。实验结束后
+601-604 的容器全部删除，32 张 GPU 均回到 0 MiB / 0% utilization。
+
+### PR 级回归
+
+真实 serving 发现 `mxfp8_quantize` 要求连续输入，因此 SGLang runner 在量化
+边界显式 materialize contiguous tensor，并把原 Kimi test 改为构造非连续
+输入。GPU6 定向测试结果：
+
+```text
+1 passed, 5 deselected in 20.22s
+```
+
+FlashInfer 官方 `tests/moe/test_trtllm_cutlass_fused_moe.py` 增加 SiTU 的
+MXFP8×MXFP4 参数组，覆盖 FP16/BF16 与 autotune on/off：
+
+```text
+4 passed, 153 deselected in 8.50s
+```
+
+候选提交：
+
+| 仓库 | 提交 | 内容 |
+|---|---|---|
+| SGLang | `8a2d501` | 非连续输入修复与回归 |
+| FlashInfer | `e0bc653` | 官方 SiTU MXFP8×MXFP4 测试 |
+
+服务器日志：
+
+```text
+/data/hzy/phase5_sglang_noncontiguous_gpu6_v2.log
+/data/hzy/phase5_flashinfer_official_situ_gpu6.log
+```
diff --git a/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/decision_log.md b/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/decision_log.md
index 061c387..6540491 100644
--- a/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/decision_log.md
+++ b/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/decision_log.md
@@ -2,6 +2,76 @@
 
 本文件记录可恢复的关键决策、失败尝试和证据路径。成功结论见 `README.md`。
 
+## 2026-08-18 Phase 5
+
+### 真实 serving 暴露的兼容问题
+
+- Kimi 基线镜像早于通用 `MoeRunnerConfig.gemm1_beta` 字段，首次
+  FlashInfer EP4 启动在权重处理阶段抛出 `AttributeError`。镜像兼容补丁
+  改为 `getattr(..., "gemm1_beta", None)`，SiTU 的 beta 仍从 Kimi 已有的
+  `gemm1_clamp_limit=25` 读取；没有改变新版本源码的通用字段语义。
+- 第二次启动完成权重和 KV cache 初始化后，autotune 收到非连续 Prefill
+  hidden states，`mxfp8_quantize` 抛出 `RuntimeError: input must be contiguous`。
+  修复位于 FlashInfer runner 边界，在量化前执行 `x.contiguous()`；正式
+  24 点矩阵验证了该路径。
+- benchmark 容器最初尝试访问 Hugging Face。入口增加
+  `--tokenizer /data/hf_models/Kimi-K3`，正式矩阵完全使用本地 tokenizer。
+
+### EP4 正式矩阵
+
+- Run ID：`kimi3-ep4-moe-full-20260818-151349`。
+- 口径：TP32/EP4/DP1，16K 输入、1 token 输出、40 requests，
+  Marlin/FlashInfer MXFP4，chunk 8K/16K，C8/C16，每点三次。
+- 结果：24/24 raw JSONL 均为 40/40 成功且 errors 为空；服务与 bench 日志
+  未发现 OOM、Traceback、NCCL error 或 EngineDeadError。
+- 8K chunk：FlashInfer Input TPS 提升 11.00%-11.08%，TTFT P50 降低
+  9.92%-9.99%。
+- 16K chunk：FlashInfer Input TPS 提升 7.63%-7.65%，TTFT P50 降低
+  7.04%-7.09%。
+- 决策：该 16K/1 Prefill 工作负载采用 EP4 + FlashInfer MXFP4 + 8K chunk。
+
+### EP32 证据处理
+
+- 不补跑 EP32。仅复用已有 `kimi3-moe-prefill-20260818-130900` 中
+  Marlin/8K/C8 两次结果。
+- EP32 中位数为 Input TPS 2531.43、TTFT P50 50.52 s；EP4 三次中位数为
+  2935.02、43.51 s，即 EP4 吞吐 +15.94%、TTFT P50 -13.88%。
+- EP32 只有两次重复，因此只作为方向性同口径对照，不替代 EP4 正式矩阵。
+
+### 原始证据与清理
+
+```text
+/data/hzy/sskj/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/results/kimi3-ep4-moe-full-20260818-151349/
+/data/hzy/kimi3-ep4-moe-full-20260818-151349.log
+```
+
+仓库内保留小型汇总：
+
+```text
+results/phase5_ep4_moe_backend_prefill_summary.json
+results/phase5_ep4_moe_backend_prefill_results.csv
+```
+
+运行结束后 601-604 无实验容器，32 张 GPU 均为 0 MiB / 0% utilization。
+
+### PR 回归补充
+
+- SGLang runner 增加非连续输入回归。第一次测试把
+  `expected=torch.empty_like(noncontiguous_x)` 也创建成非连续 stride，底层
+  kernel 按连续输出 ABI 写入后被测试错误解读，导致数值错位。reference
+  输出改为 `memory_format=torch.contiguous_format` 后通过。
+- SGLang 最终结果：`1 passed, 5 deselected`，提交 `8a2d501`。
+- FlashInfer 官方 `test_trtllm_cutlass_fused_moe.py` 增加 SiTU
+  `(alpha=4, beta=25)` 的 MXFP8×MXFP4 case，覆盖 FP16/BF16 和 autotune
+  on/off；最终 `4 passed, 153 deselected`，提交 `e0bc653`。
+- 证据日志：
+
+```text
+/data/hzy/phase5_sglang_noncontiguous_gpu6.log
+/data/hzy/phase5_sglang_noncontiguous_gpu6_v2.log
+/data/hzy/phase5_flashinfer_official_situ_gpu6.log
+```
+
 ## 2026-08-18 Phase 4
 
 ### 对称 A/B
diff --git a/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/results/phase5_ep4_moe_backend_prefill_results.csv b/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/results/phase5_ep4_moe_backend_prefill_results.csv
new file mode 100644
index 0000000..50044c7
--- /dev/null
+++ b/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/results/phase5_ep4_moe_backend_prefill_results.csv
@@ -0,0 +1,25 @@
+backend,chunked_prefill_size,concurrency,repeat,completed,duration,request_throughput,input_throughput,output_throughput,total_throughput,mean_ttft_ms,median_ttft_ms,p95_ttft_ms,p99_ttft_ms,mean_e2e_latency_ms,median_e2e_latency_ms,p95_e2e_latency_ms,p99_e2e_latency_ms
+flashinfer_mxfp4,16384,16,1,40,216.50833019288257,0.1847503971988739,3026.95050770635,0.1847503971988739,3027.135258103549,72316.50786341052,84522.44199160486,84848.25946678175,91778.32209909335,72316.55178854708,84522.48549158685,84848.3054406708,91778.3653298742
+flashinfer_mxfp4,16384,16,2,40,216.24680145387538,0.1849738342073552,3030.6112996533075,0.1849738342073552,3030.796273487515,72215.42132570758,84481.09910136554,84765.86677560116,91561.01098880405,72215.46733585419,84481.14997893572,84765.91166217111,91561.05765777174
+flashinfer_mxfp4,16384,16,3,40,216.48059168993495,0.18477406998818618,3027.3383626864425,0.18477406998818618,3027.5231367564306,72312.7285073162,84540.76406499371,84853.01167063879,91778.99107307894,72312.77419594117,84540.81119748298,84853.05667960782,91779.0367784421
+flashinfer_mxfp4,16384,8,1,40,216.53949848609045,0.18472380456985968,3026.514814072581,0.18472380456985968,3026.6995378771508,40339.51893080957,42272.18415902462,42653.667874005616,49590.42487496044,40339.55953667755,42272.22537947819,42653.713031823274,49590.473274949465
+flashinfer_mxfp4,16384,8,2,40,216.4690438960679,0.18478392697666732,3027.4998595857173,0.18478392697666732,3027.684643512694,40325.29723601765,42272.719990462065,42599.55736350965,49530.63742398982,40325.33475726959,42272.74997299537,42599.60809347685,49530.707661700435
+flashinfer_mxfp4,16384,8,3,40,216.46678952896036,0.18478585138644807,3027.531389115565,0.18478585138644807,3027.7161749669513,40328.56577691855,42263.43939546496,42628.12673794104,49566.40897058882,40328.609929577215,42263.489623437636,42628.16974768647,49566.45701683592
+flashinfer_mxfp4,8192,16,1,40,201.0955079239793,0.1989104600741321,3258.9489778545803,0.1989104600741321,3259.147888314654,66464.03402339201,78428.61395492218,80900.29283260228,82248.80441807676,66464.07695321832,78428.6568548996,80900.33666997915,82248.85000464972
+flashinfer_mxfp4,8192,16,2,40,201.02179174800403,0.1989834020091862,3260.144058518507,0.1989834020091862,3260.343041920516,66441.70982562937,78389.55687300768,80875.61333409976,82222.40279711317,66441.75368165597,78389.59934201557,80875.65720523708,82222.44497414678
+flashinfer_mxfp4,8192,16,3,40,200.99849134706892,0.19900646881439046,3260.5219850549734,0.19900646881439046,3260.7209915237877,66426.99547338416,78381.78810151294,80856.50636442006,82182.5564461993,66427.0412968297,78381.83325249702,80856.55405281577,82182.60281610303
+flashinfer_mxfp4,8192,8,1,40,201.29355681990273,0.19871475586169896,3255.7425600380757,0.19871475586169896,3255.9412747939377,37166.8226861686,39200.42008545715,41681.43365625292,43097.31771401828,37166.86569513404,39200.45927807223,41681.47955433233,43097.35930965515
+flashinfer_mxfp4,8192,8,2,40,201.1566553460434,0.1988499954485188,3257.958325428532,0.1988499954485188,3258.157175423981,37137.24017466302,39192.07067997195,41617.94732188573,43050.63308592187,37137.28631501435,39192.11111799814,41617.99226633739,43050.676568434574
+flashinfer_mxfp4,8192,8,3,40,201.02496831398457,0.198980257703726,3260.0925422178466,0.198980257703726,3260.29152247555,37112.54617974046,39172.589769004844,41604.221663821954,43020.16220120946,37112.590136541985,39172.621404984966,41604.265929351095,43020.20668042591
+marlin,16384,16,1,40,232.999846650986,0.17167393272973525,2812.7057138439823,0.17167393272973525,2812.877387776712,77838.70012289844,90956.05235511903,91350.91751561266,98844.17919690021,77838.74413453159,90956.10008307267,91350.963561167,98844.22676438
+marlin,16384,16,2,40,233.0310456538573,0.17165094842948833,2812.329139068737,0.17165094842948833,2812.5007900171663,77849.1269534803,90990.29910599347,91359.9561718176,98847.1738615795,77849.17135268915,90990.34174648114,91360.00232317018,98847.21923549892
+marlin,16384,16,3,40,233.0339416109491,0.1716488152905216,2812.2941897199057,0.1716488152905216,2812.4658385351963,77849.66164607904,90972.34335646499,91361.77453174023,98851.06839299435,77849.70525740064,90972.38598857075,91361.8199098506,98851.1173977633
+marlin,16384,8,1,40,233.02748973597772,0.17165356776284363,2812.37205422643,0.17165356776284363,2812.543707794193,43413.6843872373,45488.352137384936,45877.185943827484,53396.961687700816,43413.72910180944,45488.39849594515,45877.235028997515,53397.00680534588
+marlin,16384,8,2,40,232.74746951600537,0.17186008545304213,2815.755640062642,0.17186008545304213,2815.927500148095,43359.47861029417,45474.66833144426,45731.592441652894,53163.52174516069,43359.524680202594,45474.7117330553,45731.640505709205,53163.57911798171
+marlin,16384,8,3,40,232.98255807999521,0.1716866718677966,2812.9144318819795,0.1716866718677966,2813.086118553847,43401.67168469634,45470.05465638358,45861.754696990814,53373.858650114846,43401.71520576696,45470.09659849573,45861.829353275236,53373.90170200961
+marlin,8192,16,1,40,223.29927809908986,0.17913179272460458,2934.8952919999215,0.17913179272460458,2935.074423792646,73815.6138905033,87082.98856834881,89829.93190474808,91355.88917817222,73815.65661169589,87083.03174446337,89829.9770831829,91355.93388687819
+marlin,8192,16,2,40,223.2892873159144,0.17913980773921837,2935.0266099993537,0.17913980773921837,2935.205749807093,73812.15487574227,87086.07016585302,89856.62238476798,91390.91669618152,73812.19822114217,87086.11152344383,89856.66677479167,91390.96168792807
+marlin,8192,16,3,40,223.33232814306393,0.1791052837383063,2934.4609687684106,0.1791052837383063,2934.640074052149,73832.87409407203,87089.25215096679,89883.80955031607,91406.37799005723,73832.91684102733,87089.2927980749,89883.8516859687,91406.42066749977
+marlin,8192,8,1,40,223.2894644900225,0.17913966559666036,2935.0242811356834,0.17913966559666036,2935.20342080128,41227.18363396125,43507.58656300604,46237.23470707191,47776.81458188454,41227.227372379275,43507.62654503342,46237.28018605616,47776.86155497795
+marlin,8192,8,2,40,223.2271411139518,0.17918968007380884,2935.843718329284,0.17918968007380884,2936.0229080093577,41215.50619005575,43508.31371441018,46201.26801881706,47751.084343302064,41215.55230935919,43508.363836910576,46201.314437552355,47751.12928038696
+marlin,8192,8,3,40,223.2904916331172,0.17913884154871654,2935.0107799341718,0.17913884154871654,2935.189918775721,41230.31144623528,43496.72723049298,46221.18599918904,47788.806532013696,41230.35565857426,43496.76744209137,46221.24284039019,47788.8507199334
diff --git a/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/results/phase5_ep4_moe_backend_prefill_summary.json b/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/results/phase5_ep4_moe_backend_prefill_summary.json
new file mode 100644
index 0000000..ac85d69
--- /dev/null
+++ b/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/results/phase5_ep4_moe_backend_prefill_summary.json
@@ -0,0 +1,211 @@
+{
+  "run_id": "kimi3-ep4-moe-full-20260818-151349",
+  "summary": [
+    {
+      "backend": "flashinfer_mxfp4",
+      "chunked_prefill_size": 8192,
+      "concurrency": 8,
+      "repeats": 3,
+      "completed_each": [
+        40,
+        40,
+        40
+      ],
+      "median_request_throughput": 0.1988499954485188,
+      "median_input_throughput": 3257.958325428532,
+      "median_total_throughput": 3258.157175423981,
+      "median_mean_ttft_ms": 37137.24017466302,
+      "median_median_ttft_ms": 39192.07067997195,
+      "median_p95_ttft_ms": 41617.94732188573,
+      "median_p99_ttft_ms": 43050.63308592187,
+      "median_mean_e2e_latency_ms": 37137.28631501435,
+      "median_median_e2e_latency_ms": 39192.11111799814,
+      "median_p95_e2e_latency_ms": 41617.99226633739
+    },
+    {
+      "backend": "flashinfer_mxfp4",
+      "chunked_prefill_size": 8192,
+      "concurrency": 16,
+      "repeats": 3,
+      "completed_each": [
+        40,
+        40,
+        40
+      ],
+      "median_request_throughput": 0.1989834020091862,
+      "median_input_throughput": 3260.144058518507,
+      "median_total_throughput": 3260.343041920516,
+      "median_mean_ttft_ms": 66441.70982562937,
+      "median_median_ttft_ms": 78389.55687300768,
+      "median_p95_ttft_ms": 80875.61333409976,
+      "median_p99_ttft_ms": 82222.40279711317,
+      "median_mean_e2e_latency_ms": 66441.75368165597,
+      "median_median_e2e_latency_ms": 78389.59934201557,
+      "median_p95_e2e_latency_ms": 80875.65720523708
+    },
+    {
+      "backend": "flashinfer_mxfp4",
+      "chunked_prefill_size": 16384,
+      "concurrency": 8,
+      "repeats": 3,
+      "completed_each": [
+        40,
+        40,
+        40
+      ],
+      "median_request_throughput": 0.18478392697666732,
+      "median_input_throughput": 3027.4998595857173,
+      "median_total_throughput": 3027.684643512694,
+      "median_mean_ttft_ms": 40328.56577691855,
+      "median_median_ttft_ms": 42272.18415902462,
+      "median_p95_ttft_ms": 42628.12673794104,
+      "median_p99_ttft_ms": 49566.40897058882,
+      "median_mean_e2e_latency_ms": 40328.609929577215,
+      "median_median_e2e_latency_ms": 42272.22537947819,
+      "median_p95_e2e_latency_ms": 42628.16974768647
+    },
+    {
+      "backend": "flashinfer_mxfp4",
+      "chunked_prefill_size": 16384,
+      "concurrency": 16,
+      "repeats": 3,
+      "completed_each": [
+        40,
+        40,
+        40
+      ],
+      "median_request_throughput": 0.18477406998818618,
+      "median_input_throughput": 3027.3383626864425,
+      "median_total_throughput": 3027.5231367564306,
+      "median_mean_ttft_ms": 72312.7285073162,
+      "median_median_ttft_ms": 84522.44199160486,
+      "median_p95_ttft_ms": 84848.25946678175,
+      "median_p99_ttft_ms": 91778.32209909335,
+      "median_mean_e2e_latency_ms": 72312.77419594117,
+      "median_median_e2e_latency_ms": 84522.48549158685,
+      "median_p95_e2e_latency_ms": 84848.3054406708
+    },
+    {
+      "backend": "marlin",
+      "chunked_prefill_size": 8192,
+      "concurrency": 8,
+      "repeats": 3,
+      "completed_each": [
+        40,
+        40,
+        40
+      ],
+      "median_request_throughput": 0.17913966559666036,
+      "median_input_throughput": 2935.0242811356834,
+      "median_total_throughput": 2935.20342080128,
+      "median_mean_ttft_ms": 41227.18363396125,
+      "median_median_ttft_ms": 43507.58656300604,
+      "median_p95_ttft_ms": 46221.18599918904,
+      "median_p99_ttft_ms": 47776.81458188454,
+      "median_mean_e2e_latency_ms": 41227.227372379275,
+      "median_median_e2e_latency_ms": 43507.62654503342,
+      "median_p95_e2e_latency_ms": 46221.24284039019
+    },
+    {
+      "backend": "marlin",
+      "chunked_prefill_size": 8192,
+      "concurrency": 16,
+      "repeats": 3,
+      "completed_each": [
+        40,
+        40,
+        40
+      ],
+      "median_request_throughput": 0.17913179272460458,
+      "median_input_throughput": 2934.8952919999215,
+      "median_total_throughput": 2935.074423792646,
+      "median_mean_ttft_ms": 73815.6138905033,
+      "median_median_ttft_ms": 87086.07016585302,
+      "median_p95_ttft_ms": 89856.62238476798,
+      "median_p99_ttft_ms": 91390.91669618152,
+      "median_mean_e2e_latency_ms": 73815.65661169589,
+      "median_median_e2e_latency_ms": 87086.11152344383,
+      "median_p95_e2e_latency_ms": 89856.66677479167
+    },
+    {
+      "backend": "marlin",
+      "chunked_prefill_size": 16384,
+      "concurrency": 8,
+      "repeats": 3,
+      "completed_each": [
+        40,
+        40,
+        40
+      ],
+      "median_request_throughput": 0.1716866718677966,
+      "median_input_throughput": 2812.9144318819795,
+      "median_total_throughput": 2813.086118553847,
+      "median_mean_ttft_ms": 43401.67168469634,
+      "median_median_ttft_ms": 45474.66833144426,
+      "median_p95_ttft_ms": 45861.754696990814,
+      "median_p99_ttft_ms": 53373.858650114846,
+      "median_mean_e2e_latency_ms": 43401.71520576696,
+      "median_median_e2e_latency_ms": 45474.7117330553,
+      "median_p95_e2e_latency_ms": 45861.829353275236
+    },
+    {
+      "backend": "marlin",
+      "chunked_prefill_size": 16384,
+      "concurrency": 16,
+      "repeats": 3,
+      "completed_each": [
+        40,
+        40,
+        40
+      ],
+      "median_request_throughput": 0.17165094842948833,
+      "median_input_throughput": 2812.329139068737,
+      "median_total_throughput": 2812.5007900171663,
+      "median_mean_ttft_ms": 77849.1269534803,
+      "median_median_ttft_ms": 90972.34335646499,
+      "median_p95_ttft_ms": 91359.9561718176,
+      "median_p99_ttft_ms": 98847.1738615795,
+      "median_mean_e2e_latency_ms": 77849.17135268915,
+      "median_median_e2e_latency_ms": 90972.38598857075,
+      "median_p95_e2e_latency_ms": 91360.00232317018
+    }
+  ],
+  "comparisons": [
+    {
+      "chunked_prefill_size": 8192,
+      "concurrency": 8,
+      "flashinfer_input_throughput_change_pct": 11.002772493857949,
+      "flashinfer_total_throughput_change_pct": 11.002772493857949,
+      "flashinfer_median_ttft_ms_change_pct": -9.918996257778002,
+      "flashinfer_p95_ttft_ms_change_pct": -9.959153097854456,
+      "flashinfer_median_e2e_latency_ms_change_pct": -9.918986094468796
+    },
+    {
+      "chunked_prefill_size": 8192,
+      "concurrency": 16,
+      "flashinfer_input_throughput_change_pct": 11.082125055880665,
+      "flashinfer_total_throughput_change_pct": 11.082125055880665,
+      "flashinfer_median_ttft_ms_change_pct": -9.986112906786438,
+      "flashinfer_p95_ttft_ms_change_pct": -9.994821541601407,
+      "flashinfer_median_e2e_latency_ms_change_pct": -9.98610688810826
+    },
+    {
+      "chunked_prefill_size": 16384,
+      "concurrency": 8,
+      "flashinfer_input_throughput_change_pct": 7.628579997727458,
+      "flashinfer_total_throughput_change_pct": 7.62857999772748,
+      "flashinfer_median_ttft_ms_change_pct": -7.042347508899216,
+      "flashinfer_p95_ttft_ms_change_pct": -7.050816045775821,
+      "flashinfer_median_e2e_latency_ms_change_pct": -7.0423455840166405
+    },
+    {
+      "chunked_prefill_size": 16384,
+      "concurrency": 16,
+      "flashinfer_input_throughput_change_pct": 7.645236847665093,
+      "flashinfer_total_throughput_change_pct": 7.645236847665093,
+      "flashinfer_median_ttft_ms_change_pct": -7.089958471869751,
+      "flashinfer_p95_ttft_ms_change_pct": -7.127517325850641,
+      "flashinfer_median_e2e_latency_ms_change_pct": -7.089954195324976
+    }
+  ]
+}
\ No newline at end of file
diff --git a/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/README.md b/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/README.md
index 151eda1..d99800c 100644
--- a/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/README.md
+++ b/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/README.md
@@ -1,4 +1,4 @@
-# Kimi-K3 / Pro6000D / SGLang TP32 EP32 MoE Backend Prefill
+# Kimi-K3 / Pro6000D / SGLang TP32 EP4 MoE Backend Prefill
 
 This experiment is the real-serving acceptance gate for Kimi-K3 FlashInfer
 MXFP4 MoE on NVIDIA SM120. It does not modify the existing baseline experiment.
@@ -8,7 +8,7 @@ MXFP4 MoE on NVIDIA SM120. It does not modify the existing baseline experiment.
 | Dimension | Values |
 |---|---|
 | Nodes | 601-604, 8 GPUs per node |
-| Parallelism | TP32, EP32, DP1 |
+| Parallelism | TP32, EP4, DP1 |
 | MoE runner | `marlin`, `flashinfer_mxfp4` |
 | Input / output | 16K / 1 token |
 | Concurrency | 8, 16 |
@@ -16,7 +16,10 @@ MXFP4 MoE on NVIDIA SM120. It does not modify the existing baseline experiment.
 | Requests | 40 per repeat |
 | Repeats | 3 |
 
-The eight configurations are identical except for MoE runner and chunk size.
+The eight configurations are identical except for MoE runner, chunk size, and
+benchmark concurrency. The directory retains its historical `tp32ep32` name so
+existing evidence paths remain stable; the production comparison and the
+default `EP_SIZE` are EP4.
 The client uses SGLang `random-ids`, which produces the exact synthetic token
 length without depending on a ShareGPT file or text truncation. Radix cache is
 disabled. `OSL=1` makes TTFT, E2E latency and input TPS the primary metrics;
@@ -27,6 +30,35 @@ The defaults above can be narrowed for a capacity-first run with `EP_SIZE`,
 example, the TP32/EP4 maximum-pressure probe uses Marlin, 16K chunk, and C=16
 before spending time on the complete backend comparison.
 
+## Accepted EP4 result
+
+Run ID: `kimi3-ep4-moe-full-20260818-151349`.
+
+All 24 repeat files completed 40/40 requests with empty error arrays. No OOM,
+Traceback, NCCL error, or EngineDeadError was found. Values below are medians
+across three repeats.
+
+| Chunk | C | Backend | Input TPS | TTFT P50 | TTFT P95 |
+|---:|---:|---|---:|---:|---:|
+| 8K | 8 | Marlin | 2935.02 | 43.51 s | 46.22 s |
+| 8K | 8 | FlashInfer MXFP4 | 3257.96 | 39.19 s | 41.62 s |
+| 8K | 16 | Marlin | 2934.90 | 87.09 s | 89.86 s |
+| 8K | 16 | FlashInfer MXFP4 | 3260.14 | 78.39 s | 80.88 s |
+| 16K | 8 | Marlin | 2812.91 | 45.47 s | 45.86 s |
+| 16K | 8 | FlashInfer MXFP4 | 3027.50 | 42.27 s | 42.63 s |
+| 16K | 16 | Marlin | 2812.33 | 90.97 s | 91.36 s |
+| 16K | 16 | FlashInfer MXFP4 | 3027.34 | 84.52 s | 84.85 s |
+
+FlashInfer improves Input TPS by 11.00%-11.08% with an 8K chunk and by
+7.63%-7.65% with a 16K chunk. It reduces median TTFT by 9.92%-9.99% and
+7.04%-7.09%, respectively. The accepted Prefill setting is therefore
+`flashinfer_mxfp4`, EP4, and an 8K chunk for this 16K/1 workload.
+
+The only matching existing EP32 evidence is Marlin/8K/C8 with two repeats.
+Its median Input TPS is 2531.43 and TTFT P50/P95 are 50.52/53.64 seconds.
+Against that evidence, EP4 raises Input TPS by 15.94% and lowers TTFT P50 by
+13.88%. EP32 was not rerun.
+
 ## Unique entrypoint
 
 Run on 601 only. The sudo password is supplied at runtime and is never written
diff --git a/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/config.env b/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/config.env
index a70c281..07c8e9b 100644
--- a/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/config.env
+++ b/experiments/pro6000/kimi3_pro6000_sglang_tp32ep32_moe_backend_prefill/config.env
@@ -10,7 +10,7 @@ HEAD_HOST="${HEAD_HOST:-174.1.60.1}"
 NODE_SSH_USER="${NODE_SSH_USER:-user}"
 NODE_HOSTS=(174.1.60.1 174.1.60.2 174.1.60.3 174.1.60.4)
 DIST_PORT="${DIST_PORT:-20000}"
-EP_SIZE="${EP_SIZE:-32}"
+EP_SIZE="${EP_SIZE:-4}"
 
 BASE_IMAGE="${BASE_IMAGE:-lmsysorg/sglang:kimi-k3-fiv617situ-warm}"
 CANDIDATE_IMAGE="${CANDIDATE_IMAGE:-local/sglang:kimi-k3-sm120-flashinfer-mxfp4-phase5}"