NVIDIA B300
The NVIDIA B300 is an enterprise-class GPU built on the Blackwell architecture, featuring 288 GB of memory with 8,000 GB/s of memory bandwidth and a 1,400 W TDP. It delivers 75 TFLOPS of FP32 performance and 2,500 TFLOPS of FP16 tensor performance. Currently available from 7 providers starting at $7.10/GPU/hour, with a market median of $9.52/GPU/hour across 15 configurations.

DGX B300 · representative system
Full system with eight B300 GPUs. The illustration does not indicate the GPU count in an offer.
Hardware specifications
Same across all providers
Median price across all providers
9 offerings from 7 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 16 | 188 GB | $8.990 | $8.99 | Launch | ||
Sesterce CloudReferral link | ×8 | 120 | 2.72 TB | -- | From$4.950 | From$39.60 | |
| ×1 | Billed separately | Billed separately | -- | $7.100 0.0% 30d | From$7.10 | Launch | |
| ×8 | 224 | 1.88 TB | $7.400 | $59.20 | |||
| ×8 | 224 | 3.75 TB | $8.405 -3.3% 30d | $67.24 | Launch | ||
Up to$500credit | ×1 | 16 | 188 GB | $8.990 0.0% 30d | $8.99 | Launch | |
| ×8 | 240 | 1.99 TB | -- | From$9.524 +25.7% 30d | From$76.19 | Launch | |
| ×1 | 22 | 252 GB | $9.940 +3.2% 30d | From$9.94 | Launch | ||
LiumReferral link | ×8 | 240 | 1.96 TB | From$11.000 +43.8% 30d | From$88.00 | Launch | |
| ×8 | 240 | 1.96 TB | -- | $22.000 +29.4% 30d | $176.00 | Launch |
LLMs that fit on the NVIDIA B300
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 288 GB per GPU. Showing the 12 largest that fit.
| Model | Parameters | 16-bit | 8-bit | 4-bit |
|---|---|---|---|---|
| Kimi K3 Moonshot AI | 2.8T 104B active | — Not released | — Not released | 1,873 GB 8× · 431 GB free |
| DeepSeek V4 Pro DeepSeek | 1.6T 49B active | — Not released | — Not released | 1,038 GB 4× · 114 GB free |
| Kimi K2.5 Moonshot AI | 1T 32B active | — Not released | — Not released | 714 GB 4× · 438 GB free |
| GLM-5.2 Z.ai | 753B | 1,808 GB 8× · 496 GB free | 994 GB 4× · 158 GB free | 579 GB 4× · 573 GB free or 2× · 19% spare |
| DeepSeek R1 DeepSeek | 671B 37B active | — Not released | 826 GB 4× · 326 GB free | 481 GB 2× · 95 GB free |
| DeepSeek V4 Flash DeepSeek | 284B | — Not released | — Not released | 192 GB 1× · 96 GB free |
| Solar Open2 250B Upstage | 250B 15B active | 601 GB 4× · 551 GB free or 2× · 15% spare | 330 GB 2× · 246 GB free or 1× · 4% spare | 192 GB 1× · 96 GB free |
| Qwen3 235B-A22B Alibaba | 235B 22B active | 564 GB 2× · 12 GB free | 310 GB 2× · 266 GB free or 1× · 11% spare | 181 GB 1× · 107 GB free |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB 1× · 5.8 GB free | 155 GB 1× · 133 GB free | 90 GB 1× · 198 GB free |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 1× · 210 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 1× · 202 GB free | 47 GB 1× · 241 GB free | 28 GB 1× · 260 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 1× · 209 GB free | 43 GB 1× · 245 GB free | 25 GB 1× · 263 GB free |
Estimates: model weights plus 20% for KV cache and runtime overhead, at short context lengths. Long contexts and large batches need more. Rows marked “tight” hold the weights but not that full margin. A ×N figure is the total VRAM across N of these GPUs and makes no claim about interconnect throughput. How we estimate this · Start from a model instead
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