Self-Hosted Model Expertise: VLLM Setup, Quantization, and Performance Optimization

Create comprehensive documentation for deploying self-hosted LLMs with VLLM on GCP, covering architecture, quantization, and optimization techniques.

Scope:

• GCP instance provisioning
• Local install infrastructure recommendation
• Docker vs native VLLM installation with dependencies
• GPU architecture compatibility (Ada/Ampere/Hopper) and quantization methods (AWQ, GPTQ, FP8)
• Performance optimization flags (KV cache, tensor parallelism, batching...)
• Model benchmarks (SWE-bench scores for Claude 4.5, Devstral, GPT-OSS, Llama 4 Maverick)
• Cost analysis with token usage examples
• Observability setup

Deliverables:

• docs/self-hosted-models/vllm-setup-guide.md - Complete setup instructions
• scripts/gcp-provision-gpu.sh - Automated GCP provisioning script
• scripts/vllm-production-configs/ - Production configuration templates

Acceptance Criteria:

• Documentation covers both Docker and native installations
• All GPU architectures (L4/A100/H100) documented with quantization recommendations
• Performance optimization flags explained with measurable impact
• SWE-bench matrix includes all major models (Claude, Devstral, GPT-OSS, Llama 4)
• Scripts tested on clean GCP project