The KV cache is often the dominant consumer of GPU VRAM during inference. With 179 GB per NVIDIA HGX B200 GPU, efficient cache management determines how many concurrent requests you can serve and at what context length.
Extend effective memory by offloading KV cache to CPU memory when GPU HBM is insufficient.
The NVIDIA HGX B200 GPU supports both FP8 (8-bit floating point) and NVFP4 (4-bit floating point) quantization natively in hardware. These formats reduce memory usage and increase throughput compared to BF16, with minimal quality loss.
vLLM on the NVIDIA HGX B200 uses specialized CUDA kernel backends for attention and GEMM operations. Understanding these backends helps diagnose startup issues, explain warmup times, and tune performance.
Maximize throughput by tuning vLLM for concurrent request loads on NVIDIA HGX B200 GPUs.