On-Premise LLM Deployment: What It Actually Costs vs What Vendors Quote
Key Takeaways
- —GPU hardware provision or cloud VPC reservation dominates initial capital expenditure, but engineering maintenance accounts for ongoing costs.
- —Quantization techniques (AWQ, GGUF) reduce VRAM requirements by 50-70% with negligible loss in practical inference accuracy.
- —Calculate cost per 1M tokens at expected peak load rather than average baseline load to avoid capacity bottlenecks.
Sales quotes for self-hosted LLM infrastructure often highlight bare-metal GPU server pricing while omitting operational realities like redundancy, high-availability load balancing, MLOps engineering, and continuous model updating.
VRAM calculations: the true capacity metric
Model parameters dictate GPU VRAM requirements. A 70-billion parameter model running in FP16 precision requires ~140 GB of VRAM just to load weights into memory, excluding KV-cache overhead for concurrent user requests.
Utilizing 4-bit or 8-bit quantization (such as AWQ or GPTQ) reduces VRAM footprint down to ~40-45 GB, allowing deployment on dual NVIDIA L40S or A10G instances rather than multi-node H100 clusters.
Infrastructure vs operational labor costs
While monthly cloud GPU reservation (e.g., AWS g5.12xlarge or dedicated bare-metal) ranges from $1,500 to $4,500 per node, engineering overhead for setup, security patch management, uptime monitoring, and fallback routing typically represents the larger operational investment for mid-sized engineering teams.
When on-premise infrastructure ROI turns positive
Self-hosted infrastructure reaches financial parity with commercial API consumption when processing continuously exceeds 15-20 million tokens daily, or when legal compliance renders cloud API transmission unacceptable regardless of cost.