How much VRAM does DeepSeek V4-Pro need?

About DeepSeek V4-Pro

How much VRAM does a DeepSeek V4-Pro need? At bf16 — the reference precision, no quality loss — a reference 8K-context, batch-1 deployment needs 3205.2GB, which is enough to tell you the smallest fitting configuration is 16× B300 SXM 288GB. That number comes straight out of our GPU calculator's architecture-aware model, the same path the interactive calculator uses, not a size-based estimate. DeepSeek V4-Pro is a DeepSeek model built from 1600B total parameters, with only 49.0B active per token via mixture-of-experts routing, and most of that VRAM at batch size 1 is model weights rather than KV cache or runtime overhead — which is exactly why quantization moves the needle so much. Switching to fp8 cuts the weight footprint roughly in half; AWQ (weight-only int4) goes further, at some quality cost on harder tasks; and NVFP4 lands in a similar range to AWQ but with native throughput on Blackwell GPUs instead of an emulated dtype. The table above walks through all four options with the exact VRAM total and the smallest GPU configuration each one unlocks. Context length adds its own pressure on top of the weights: every additional token of context grows the KV cache, so a 128K-context deployment of this model needs meaningfully more headroom than the 8K reference case shown as the headline number — the context-sensitivity table breaks that out at 32K and 128K wherever this model's native window allows it.