Abstract: Qwen3.8-27B offers top performance in reasoning and vision, but its scale strains 24GB GPUs. This guide covers hardware sizing, vLLM tuning, and OOM prevention.1. Hardware BenchmarksHardwareRuntimeQuantizationContextSpeed1x RTX 4090 (24GB)OllamaGGUF Q4_K_M16k25-35 tok/s2x RTX 4090 (48GB)vLLMAWQ (INT4)32k-64k60-90 tok/s1x A100 (80GB)vLLMFP16 Native64k-128k80-120 tok/s2. Ollama Local SetupCreate Modelfile.qwen38:FROM qwen3.8:27b-instruct-q4_K_M
PARAMETER num_ctx 16384
PARAMETER temperature 0.6
Run: ollama create qwen3.8-local -f ./Modelfile.qwen383. Production vLLM Deploymentdocker run --gpus ‘“device=0,1”’ -d -p 8000:8000
vllm/vllm-openai:latest
–model /models/Qwen3.8-27B-AWQ
–tensor-parallel-size 2
–max-model-len 32768
–gpu-memory-utilization 0.92
–quantization awq
–enable-chunked-prefill
–tensor-parallel-size 2: Cuts latency across GPUs.–enable-chunked-prefill: Prevents KV cache lockups.4. Troubleshooting (FAQ)OOM Error: Set --max-model-len explicitly and install flash-attn.Slow Dual GPUs: PCIe bandwidth bottlenecks Tensor Parallelism. Switch to --pipeline-parallel-size 2.Vision Overload: Limit image tokens with --limit-mm-per-prompt image=4.