GPU Infrastructure for LLM Deployment: What You Need to Know

The GPU Landscape for LLM Inference

Choosing the right GPU infrastructure for LLM deployment is one of the most consequential technical decisions an enterprise will make. Under-provision and your models will be too slow for production. Over-provision and you're burning capital on idle compute.

H100 vs. A100: The Key Decision

Both GPUs are workhorses for LLM inference, but they serve different tiers:

• NVIDIA H100 (Hopper): 80GB HBM3, 3.35 TB/s bandwidth, native FP8 via Transformer Engine. 2-3x faster than A100 for LLM workloads. The choice for latency-sensitive production.
• NVIDIA A100 (Ampere): 80GB HBM2e, 2 TB/s bandwidth, no native FP8. Proven, reliable, and significantly cheaper per GPU. Excellent for cost-conscious deployments with moderate throughput needs.

The Memory Equation

VRAM is the primary bottleneck. A 70B parameter model at FP16 requires approximately 140GB of VRAM just for weights - before accounting for the KV cache, activations, and framework overhead. With FP8 quantization, that drops to 70GB, fitting on a single H100.

But weights are only part of the story. The KV cache grows with context length and batch size. At 128K context with 8 concurrent users, the KV cache alone can exceed 40GB. Plan your VRAM budget to include weights + KV cache + 20% overhead.

Scaling Strategies

Tensor Parallelism

Split a single model across multiple GPUs within a node. Requires NVLink for low-latency communication. Use for models that don't fit on a single GPU.

Pipeline Parallelism

Split model layers across GPUs. Lower bandwidth requirements than tensor parallelism, but introduces pipeline bubbles. Best for multi-node deployments.

Replica Scaling

Run multiple independent model replicas behind a load balancer. The simplest scaling strategy when each replica fits in available VRAM. Scale horizontally based on throughput requirements.

Cost Optimization

The most impactful cost optimization is aggressive quantization. FP8 cuts your GPU count in half compared to FP16 with minimal quality impact. Combine with KV cache quantization and efficient inference engines (vLLM, TensorRT-LLM) to maximize tokens-per-dollar.

For sovereign deployments, owning GPU infrastructure eliminates per-token API costs entirely. The break-even point typically arrives within 6-12 months for enterprises with sustained inference workloads.