Size and configure GPUs for vLLM inference. Master memory requirements, KV cache, quantization, and tensor parallelism for LLM deployment.

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vLLM inference performance depends on understanding prefill (memory-bandwidth bound) and decode (compute-bound) phases. GPU selection requires calculating VRAM needs based on model weights (parameters × bytes per parameter) plus KV cache (grows with context length and concurrency). Quantization (FP16→INT4) reduces memory by 75%, enabling large models on smaller GPUs. FP8 offers optimal speed-quality balance on modern hardware. Tensor Parallelism pools VRAM across GPUs but adds communication overhead. A 70B FP16 model needs 140GB for weights alone; INT4 quantization reduces this to 35GB. KV cache for 70B with 32k context and 10 users requires ~112GB (FP16) or 56GB (FP8). Always reserve 4-8GB VRAM overhead for system operations.

How to Choose the Right GPU for vLLM Inference

The Anatomy of vLLM Runtime Behavior: Prefill vs. Decode

Sizing Fundamentals: How Models, Precision, and Hardware Determine Fit

GPU Hardware Characteristics & Constraints

Putting the Numbers to the Test: Sizing Scenarios

Quantization: The Art of “Squeezing” Models

Putting It All Together: From Requirements to a Deployment Plan

Practical Use-Cases of vLLM GPU Inference