391866785e44a6eae719dcdc7aad29b980a81e6a
From competitor docker log analysis: - max_model_len: 100000 → 256000 (competitor proven, 19259 GPU blocks) - gpu_memory_utilization: 0.90 → 0.95 (competitor proven) - max_num_batched_tokens: None → 4096 (competitor proven) - enable_chunked_prefill: off → on (competitor proven, critical for 256K context) - max_num_seqs stays at 2 (matches competitor) Competitor Sub168 scored 60194 with these exact params before OOM at replay tail. Our code has OOM-surviving advantages they lack (n>1 clamp, max_completion_tokens). Docker log evidence: competitor's vLLM started with 19259 GPU blocks at 0.95 util, ran for ~1h18m before OOM in layernorm.py x.float() at 31.72 GiB GPU.
project_6
Description
Languages
C++
41.8%
Cuda
31.6%
Python
22.2%
C
2.1%
CMake
1.1%
Other
1.1%