B300 SXM 288GB has 288 GB of VRAM and 8,000 GB/s of memory bandwidth, plus 1800 GB/s of NVLink. It runs 40 models from the catalog on a single card — up to Llama 3.1 405B — and serves Llama 3.3 70B at 1326 tok/s aggregate. All figures are computed by tokcalc's open-source capacity model.
| Vendor | NVIDIA |
| Category | datacenter |
| VRAM | 288 GB |
| Memory bandwidth | 8,000 GB/s |
| FP16/BF16 dense compute | not publicly reported |
| NVLink bandwidth | 1800 GB/s |
| Release year | 2025 |
| Typical cloud price | $8/hr |
| Note | Announced; FP16 dense not published |
| Model | Params | Quant | VRAM needed | Aggregate tok/s | Cost / 1M tok |
|---|---|---|---|---|---|
| GTE Large (1024-dim) | 0.44B | fp16 | 7.32 GB | 70909 | $0.0313 |
| Multilingual E5 Large (1024-dim) | 0.56B | fp16 | 7.56 GB | 55714 | $0.0399 |
| BGE-M3 (embed, 1024-dim) | 0.57B | fp16 | 7.58 GB | 54930 | $0.0405 |
| SmolLM2 1.7B Instruct | 1.70B | fp16 | 16.28 GB | 18353 | $0.1211 |
| Phi-3 Mini 3.8B | 3.80B | fp16 | 33.37 GB | 8211 | $0.2707 |
| Qwen 3 4B | 4B | fp16 | 17.66 GB | 7800 | $0.2849 |
| Llama 2 7B | 6.74B | fp16 | 47.84 GB | 4629 | $0.4801 |
| Qwen 2.5-VL 7B (VLM) | 7B | fp16 | 17.76 GB | 4457 | $0.4986 |
| Mistral 7B v0.3 | 7.24B | fp16 | 23.07 GB | 4309 | $0.5157 |
| Qwen2 7B | 7.62B | fp16 | 19.00 GB | 4094 | $0.5427 |
| Qwen 3 8B | 8B | fp16 | 25.66 GB | 3900 | $0.5698 |
| Llama 3 8B | 8.03B | fp16 | 24.65 GB | 3885 | $0.5719 |
| Gemma 2 9B | 9B | fp16 | 40.55 GB | 3467 | $0.6410 |
| Falcon 3 10B Instruct | 10B | fp16 | 30.74 GB | 3120 | $0.7123 |
| Pixtral 12B (VLM) | 12B | fp16 | 34.74 GB | 2600 | $0.8547 |
| OLMo 2 13B | 13B | fp16 | 36.74 GB | 2400 | $0.9259 |
| Phi-4 14B | 14B | fp16 | 38.74 GB | 2229 | $0.9972 |
| Qwen 2.5 14B | 14B | fp16 | 40.88 GB | 2229 | $0.9972 |
| Qwen 3 14B | 14B | fp16 | 38.74 GB | 2229 | $0.9972 |
| GPT-NeoX 20B | 20B | fp8 | 90.87 GB | 4680 | $0.4748 |
| GPT-OSS 20B (MoE) | 20.91B | fp8 | 22.55 GB | 26000 | $0.0855 |
| Codestral 25.08 | 22B | fp8 | 37.03 GB | 4255 | $0.5223 |
| Gemma 2 27B | 27B | fp8 | 51.70 GB | 3467 | $0.6410 |
| Qwen3.8 27B (VLM) | 27.78B | fp8 | 33.28 GB | 3369 | $0.6596 |
| Qwen 3 30B-A3B (MoE) | 30B | fp8 | 36.44 GB | 31200 | $0.0712 |
| Qwen 3 32B | 32B | fp8 | 49.18 GB | 2925 | $0.7597 |
| Mixtral 8x7B (MoE) | 46.70B | fp8 | 55.29 GB | 7256 | $0.3063 |
| Llama 3.3 70B | 70.60B | fp8 | 92.07 GB | 1326 | $1.68 |
| Llama 3 70B | 70.60B | fp8 | 92.07 GB | 1326 | $1.68 |
| Qwen 2.5-VL 72B (VLM) | 72B | fp8 | 93.47 GB | 1300 | $1.71 |
| Qwen 2.5 72B | 72.70B | fp8 | 94.17 GB | 1287 | $1.73 |
| Qwen2 72B | 72.70B | fp8 | 94.17 GB | 1287 | $1.73 |
| Llama 4 Scout 17B-16E (MoE, VLM) | 109B | awq4 | 72.83 GB | 5873 | $0.3784 |
| GPT-OSS 120B (MoE) | 116.83B | awq4 | 66.71 GB | 19576 | $0.1135 |
| Mixtral 8x22B (MoE) | 141B | awq4 | 92.58 GB | 2560 | $0.8681 |
| Mistral Large 3 (256K) | 235B | awq4 | 150.19 GB | 1406 | $1.58 |
| Qwen 3 235B-A22B (MoE) | 235B | awq4 | 141.87 GB | 4538 | $0.4897 |
| DeepSeek Coder V2 236B (MoE) | 236B | awq4 | 142.42 GB | 4754 | $0.4674 |
| Llama 4 Maverick 17B-128E (MoE, VLM) | 400B | awq4 | 232.88 GB | 5873 | $0.3784 |
| Llama 3.1 405B | 405B | awq4 | 256.57 GB | 246.5 | $9.01 |
| GPU | Aggregate tok/s | Cost / 1M tok | $/hr |
|---|---|---|---|
| Cerebras CS-3 (WSE-3) | 45076 | — | — |
| RTX 4080 16GB | 6464 | $0.1031 | $0.3 |
| RTX A4000 16GB | 4039 | $0.1651 | $0.3 |
| RTX 5090 32GB | 4039 | $0.1376 | $0.5 |
| A100 40GB SXM4 | 3493 | $0.2863 | $0.9 |
| RTX 4090 24GB | 2272 | $0.1956 | $0.4 |
| V100 SXM2 32GB | 2028 | $0.2739 | $0.5 |
| RTX 3090 24GB | 2017 | $0.1377 | $0.25 |
| H100 SXM5 80GB | 1888 | $0.7358 | $2.5 |
| TPU v6e Trillium (32GB) | 1846 | $0.4816 | $0.8 |
| T4 16GB | 1836 | $0.2421 | $0.2 |
| A10G 24GB | 1352 | $0.2958 | $0.36 |
B300 SXM 288GB is a datacenter accelerator from NVIDIA with 288 GB of VRAM and 8,000 GB/s of memory bandwidth and 1800 GB/s of NVLink, typically around $8/hr on cloud rental. Announced; FP16 dense not published. It is aimed at production LLM serving.
40 models in the tokcalc catalog fit on one B300 SXM 288GB, up to Llama 3.1 405B (405B params) at awq4. Below that, throughput and cost per million tokens improve as the model gets smaller.
At fp8 with 8 concurrent users, 1× B300 SXM 288GB serves 1326 tokens/sec aggregate (110.5 tok/s per stream), with a 1.58 s time to first token. That works out to $1.68 per 1M output tokens.
Around $8/hr for on-demand cloud rental (a typical 2025–2026 estimate across RunPod, Lambda, Modal, AWS, GCP and similar providers). Actual pricing varies by region, commitment, and spot availability — the Build vs Buy tab in the calculator pulls live quotes from several providers.
Memory bandwidth drives decode speed and VRAM drives capacity. B300 SXM 288GB has 8,000 GB/s of bandwidth and 288 GB of VRAM. The comparison table on this page ranks every accelerator in the catalog on the same reference workload so you can see where it lands.
Pick your model, context length, and concurrency — the calculator computes the rest with the same formulas.