GB300 NVL72
Rack scale, 72 liquid-cooled GPUs
| Indicative price | on quotation: neither NVIDIA nor its integrators publish a price for the rack; analyst estimates reported in the press range from $3.7M to $6.5M and are not prices (price guide) |
|---|---|
| GPU | 72 Blackwell Ultra and 36 Grace (2,592 Arm Neoverse V2 cores) |
| Memory | 20 TB HBM3e per NVIDIA (72 × 288 GB, i.e. 20,736 GB), 37 TB of fast memory |
| Compute | 1.08 ExaFLOPS dense FP4 (1.44 with sparsity) |
| NVLink | 130 TB/s across a 72-GPU domain |
| Power draw | not published by NVIDIA; liquid cooling |
| Form factor | full 42U rack |
What the machine runs
A single 72-chip supercomputer serves frontier models and a private AI cloud. Recommended runtime: disaggregated vLLM with LiteLLM.
Positioning
Large enterprises host their own frontier model in a single rack, in a sovereign AI cloud.
Manufacturers
Rack integration from Dell, HPE, Lenovo, Supermicro, ASUS and Gigabyte, with liquid cooling.
GPUDirect Storage
On platforms equipped with ConnectX cards, GPUDirect Storage technology establishes a direct path between NVMe or NVMe over Fabric storage and GPU memory, bypassing the CPU buffer. Compatible storage arrays, such as NetApp, VAST, DDN or WEKA, feed training and large-scale RAG at full speed.
Support
Open stack supported by QDNA and the communities. NVIDIA AI Enterprise option (NIM, NeMo, Triton) with a service-level agreement, licensed per GPU.
Which models fit on GB300 NVL72?
The machine offers 20,700 GB of memory. 7 of the 7 open models in the catalogue load on it, in the format shown.
| Model | Billion parameters | Most precise format that fits | Sizing page |
|---|---|---|---|
| GLM 5.2 | 744 | FP16 | GLM 5.2 on GB300 NVL72 |
| Kimi K3 | 2,800 | FP16 | Kimi K3 on GB300 NVL72 |
| Kimi K2.7 Code | 1,000 | FP16 | Kimi K2.7 Code on GB300 NVL72 |
| DeepSeek V4 | 1,600 | FP16 | DeepSeek V4 on GB300 NVL72 |
| Nemotron 3 Ultra | 550 | FP16 | Nemotron 3 Ultra on GB300 NVL72 |
| MiniMax M3 | 428 | FP16 | MiniMax M3 on GB300 NVL72 |
| Qwen 3.8 27B | 27 | FP16 | Qwen 3.8 27B on GB300 NVL72 |
See the full sizing matrix.
Frequently asked questions
How much does a configured gb300 nvl72 cost for an LLM?
Pricing depends on configuration and supplier. No public price is listed for this configuration; we provide a detailed quote after a scoping call.
Which LLM fits in a gb300 nvl72?
Depends on the quantization format and the input context. The table on the page indicates the recommended maximum model size.
Which runtimes support the gb300 nvl72?
vLLM, llama.cpp, Triton Inference Server, depending on the chosen framework. The model, format and GPU combinations measured by QDNA are published in the measurements section.