under test, not sure no errors

docs/server_recon/machine_profile.md

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+# Competition Server Profile
+**Captured**: 2026-08-01
+
+## Hardware
+- **GPU**: 4× Iluvatar BI-V100 32GB HBM each (128GB total)
+  - Clock: SM 1500MHz / Mem 1200MHz
+  - Driver: 3.2.1, COREX 10.2
+  - Power: 250W TDP per card
+- **CPU**: Intel Xeon Gold 6530
+- **RAM**: 503GB DDR
+- **Disk**: 3.5TB overlay, 100GB JuiceFS (public-storage)
+
+## Software
+- **OS**: Ubuntu 20.04.6 LTS, kernel 5.15.0-119
+- **COREX**: 3.2.3 at `/usr/local/corex`
+- **torch**: 2.1.0+corex.3.2.3
+- **vllm**: 0.6.3+corex.3.2.3
+- **transformers**: 4.51.3
+
+## Model
+- **Path**: `/root/public-storage/models/Qwen/Qwen3.6-35B-A3B/`
+- **Name**: Qwen3.6-35B-A3B (MoE, 35B total, 3B active)
+- **Note**: 4 cards × 32GB = 128GB total, model fits
+
+## Key Paths
+- `/root/llm-infer/` — benchmark scripts, README
+- `/root/public-storage/models/Qwen/` — model weights
+- `/root/apps/llm-modelzoo/benchmark/vllm/` — benchmark tools
+- `/share/fshare/common/models/` — shared model storage (NFS)
+
+## Benchmark Tools
+- `benchmark_server_v0.5.0.py` — automated server benchmark
+  - Sweeps: max-num-seqs=[128,256] × num-prompts=[1,128] × input=[128,1024] × output=[128,1024]
+- `benchmark_server_v0.5.0.sh` — launches vllm server + benchmark client
+  - Sets `NCCL_FORCESYNC_DISABLE=1`
+  - Auto-cleanup of vllm processes
+- `benchmark_serving_tokens.py` — online serving benchmark client
+
+## Scoring Formula
+`Output TPS × 16.796 + Input TPS × 2.799 + Cache TPS × 0.56`
+- Threshold: ≥ 8000 weighted score
+- Output TPS weight: 83% of total score

docs/server_recon/qwen36_bootstrap_issue.md

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+# Qwen3.6-35B-A3B Bootstrap Issue
+
+## Problem
+vllm 0.6.3+corex.3.2.3 does not recognize `qwen3_5_moe` model type.
+
+```
+ValueError: The checkpoint you are trying to load has model type `qwen3_5_moe` 
+but Transformers does not recognize this architecture.
+```
+
+## Root Cause
+- Model `config.json` specifies `"model_type": "qwen3_5_moe"` and `"architectures": ["Qwen3_5MoeForCausalLM"]`
+- Server transformers version: 4.51.3 (needs ≥ 4.57.1)
+- Server vllm version: 0.6.3+corex.3.2.3
+
+## Model Architecture (from config.json)
+- **Type**: Qwen3_5MoeForCausalLM (MoE with linear attention)
+- **Total params**: ~35B
+- **Active params per token**: ~3B (8 of 256 experts)
+- **Hidden size**: 2048
+- **Layers**: 40 (30 linear_attention + 10 full_attention, every 4th is full)
+- **Experts**: 256 total, 8 per token
+- **Expert intermediate**: 512
+- **Shared expert intermediate**: 512
+- **Head dim**: 256
+- **KV heads**: 2 (GQA ratio 8:1)
+- **Max position**: 262144
+- **Vocab**: 248320
+- **Precision**: bfloat16
+- **Linear attention**: conv kernel dim=4, 16 key heads (dim128), 32 value heads (dim128)
+- **MTP**: 1 hidden layer (multi-token prediction)
+- **Vision**: yes (patch16, depth27, hidden1152)
+
+## Key Architecture Features
+1. **Hybrid attention**: 3 linear_attention + 1 full_attention pattern (30+10=40 layers)
+2. **MoE**: 256 experts, top-8 routing = very sparse
+3. **Linear attention with conv**: NOT standard transformer — uses conv kernel dim=4
+4. **Multi-token prediction (MTP)**: 1 extra hidden layer for speculative prediction
+5. **Multimodal**: has vision encoder (but competition likely tests text only)
+
+## Solution Paths
+1. **EngineX route**: Check if the competition's enginex-vllm package already supports this model
+   - The repo has `enginex-vllm-bi100-qwen36-main.zip` (96MB) — THIS is likely the answer
+2. **Upgrade transformers**: `pip install transformers>=4.57.1` (may break corex compatibility)
+3. **Custom model registration**: Register Qwen3_5MoeForCausalLM in vllm's model registry