Running DeepSeek V4 on GB300 NVL72
DeepSeek V4 has 1600 billion parameters. GB300 NVL72 offers 20,700 GB of HBM3e across a 72-GPU rack memory. This page puts the two side by side.
How much memory does DeepSeek V4 need?
Weights take the parameter count multiplied by the format size, plus a 15% runtime margin for activations and buffers. In FP16 the calculation reads: 1,600 billion parameters × 2 bytes (FP16) = 3,200.0 GB of weights; × 1.15 runtime margin = 3,680.0 GB. The attention cache sits on top: it grows with context length and with the number of concurrent requests, so it is sized case by case.
| Format | Weights in memory | On this machine | What the format costs |
|---|---|---|---|
| FP16 | 3,680 GB | fits | full precision, the quality reference |
| FP8 | 1,840 GB | fits | negligible loss on most tasks |
| NVFP4 | 920 GB | fits | Blackwell format, native FP4 compute |
| Q4 | 1,104 GB | fits | block quantisation, broadly supported |
Model sheet: 1,600 billion parameters, 49 billion active per token, 1,048,576-token context, MIT licence. Pro variant, 1.6 trillion announced; the repository holds 1,599 billion elements, published in mixed FP4 and FP8 (865 GB).
DeepSeek V4 activates 49 billion parameters per token out of 1600 billion. Active parameters govern speed, not footprint: every expert stays resident in memory. Confusing the two under-sizes the machine by an order of magnitude.
What is left to serve requests?
DeepSeek V4 fits comfortably on GB300 NVL72. In FP16, its weights take about 3,680 GB of the 20,700 GB available, leaving 17,020 GB for the attention cache and concurrency. The remaining margin decides how many concurrent requests and how much context the machine sustains. By QDNA convention a machine “fits” when the weights take at most 75% of its memory; beyond that it “barely fits” and forces a limit on context or on concurrency. The attention cache is not calculated on this page. These models’ architectures (compressed latent, linear or sparse attention, Mamba layers) share no per-token formula; it is measured on the machine, with the target context and concurrency [TO BE MEASURED].
GB300 NVL72: price on quotation. NVIDIA publishes no price; analyst estimates range from $3.7M to $6.5M per rack (Loop Capital, Tom’s Hardware, as reported by Spheron on 16 August 2026). The machine targets the GE segment.
Where else can DeepSeek V4 run?
See the DeepSeek V4 and GB300 NVL72 fact sheets.
How much memory does DeepSeek V4 need?
In FP16, weights take about 3,680 GB including the runtime margin. The attention cache sits on top and depends on context.
Does DeepSeek V4 fit on GB300 NVL72?
DeepSeek V4 fits comfortably on GB300 NVL72. In FP16, its weights take about 3,680 GB of the 20,700 GB available, leaving 17,020 GB for the attention cache and concurrency.
Method and sources
Memory footprints are calculated, not measured: a reading on real hardware may differ depending on the engine and the exact weight format. Every figure below carries its source and the date of its last check.
- DeepSeek V4: 1,600 billion parameters, 49 billion active, 1,048,576-token context, MIT licence. Hugging Face repository (config.json, API, model card), checked on 2 September 2026. Pro variant, 1.6 trillion announced; the repository holds 1,599 billion elements, published in mixed FP4 and FP8 (865 GB).
- GB300 NVL72: 20,700 GB of memory; 72 Blackwell Ultra GPUs and 36 Grace CPUs, 20 TB of GPU memory per NVIDIA (72 × 288 GB = 20,736 GB), 37 TB fast memory, 130 TB/s NVLink, 1,440 PFLOPS FP4 with sparsity (1,080 dense); power draw not published. Manufacturer sheet checked on 2 September 2026.
- GB300 NVL72 price: on quotation. NVIDIA publishes no price; analyst estimates range from $3.7M to $6.5M per rack (Loop Capital, Tom’s Hardware, as reported by Spheron on 16 August 2026).
- Bytes per parameter: FP16 2 (16 bits per parameter, hence 2 bytes); FP8 1 (8 bits per parameter (E4M3), hence 1 byte, block scales not counted); NVFP4 0.5 (4 bits per parameter, hence 0.5 byte; 4-bit published weights weigh 0.54 to 0.56 byte per parameter with their scales (DeepSeek V4 Pro 865 GB for 1,599 billion, Kimi K3 1,561 GB for 2,780 billion)); Q4 0.6 (taken here as llama.cpp Q4_K_M, 0.6 byte per parameter including scales; Q4_K_S weighs 0.56, AWQ and GPTQ 0.55). Source: connaissance/faits.yaml, quantifications family, checked on 2026-08-31.
- Runtime margin × 1.15: QDNA operating assumption, not measured: 15% above the weights for activations, buffers and fragmentation.
- “Fits” threshold at 75% of memory: QDNA assumption, not measured, which keeps the remaining quarter for the attention cache and concurrency.