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runpod-ai-orchestrator/services/vllm/config_llama.yaml
Sebastian Krüger f8694653d0
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fix: adjust VRAM for 24K context based on actual usage
Based on error output, model uses ~17.5GB (not 15GB estimated).
- Llama: 85% VRAM for 24576 context (3GB KV cache)
- BGE: 6% VRAM (reduced to fit)
- Total: 91%

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-30 22:46:34 +01:00

11 lines
238 B
YAML

model: meta-llama/Llama-3.1-8B-Instruct
host: "0.0.0.0"
port: 8001
uvicorn-log-level: "info"
gpu-memory-utilization: 0.85
max-model-len: 24576
dtype: auto
enforce-eager: false
enable-auto-tool-choice: true
tool-call-parser: "llama3_json"