9b0d1c283c
Hardware: 4× Iluvatar BI-V100 32GB, Xeon Gold 6530, 503GB RAM Software: vllm 0.6.3+corex.3.2.3, torch 2.1.0+corex.3.2.3 Model: Qwen3.6-35B-A3B at /root/public-storage/models/Qwen/ Benchmark: benchmark_server_v0.5.0.py with automated sweeps
43 lines
1.5 KiB
Markdown
43 lines
1.5 KiB
Markdown
# Competition Server Profile
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**Captured**: 2026-08-01
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## Hardware
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- **GPU**: 4× Iluvatar BI-V100 32GB HBM each (128GB total)
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- Clock: SM 1500MHz / Mem 1200MHz
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- Driver: 3.2.1, COREX 10.2
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- Power: 250W TDP per card
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- **CPU**: Intel Xeon Gold 6530
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- **RAM**: 503GB DDR
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- **Disk**: 3.5TB overlay, 100GB JuiceFS (public-storage)
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## Software
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- **OS**: Ubuntu 20.04.6 LTS, kernel 5.15.0-119
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- **COREX**: 3.2.3 at `/usr/local/corex`
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- **torch**: 2.1.0+corex.3.2.3
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- **vllm**: 0.6.3+corex.3.2.3
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- **transformers**: 4.51.3
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## Model
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- **Path**: `/root/public-storage/models/Qwen/Qwen3.6-35B-A3B/`
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- **Name**: Qwen3.6-35B-A3B (MoE, 35B total, 3B active)
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- **Note**: 4 cards × 32GB = 128GB total, model fits
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## Key Paths
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- `/root/llm-infer/` — benchmark scripts, README
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- `/root/public-storage/models/Qwen/` — model weights
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- `/root/apps/llm-modelzoo/benchmark/vllm/` — benchmark tools
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- `/share/fshare/common/models/` — shared model storage (NFS)
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## Benchmark Tools
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- `benchmark_server_v0.5.0.py` — automated server benchmark
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- Sweeps: max-num-seqs=[128,256] × num-prompts=[1,128] × input=[128,1024] × output=[128,1024]
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- `benchmark_server_v0.5.0.sh` — launches vllm server + benchmark client
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- Sets `NCCL_FORCESYNC_DISABLE=1`
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- Auto-cleanup of vllm processes
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- `benchmark_serving_tokens.py` — online serving benchmark client
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## Scoring Formula
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`Output TPS × 16.796 + Input TPS × 2.799 + Cache TPS × 0.56`
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- Threshold: ≥ 8000 weighted score
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- Output TPS weight: 83% of total score
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