Thuê Nvidia B200 CC SXM6.

Blackwell 180GB VRAM 1000W

Cloud instance options

Pre-configured instances on hyperscalers.

Whole-instance bundles (GPU + vCPU + RAM + disk) on the major clouds. Per-GPU rate often drops as the count rises. View = spec page · Launch = sign up (affiliate).

Cheapest bundle

$89.65/hr

Lowest $/hr per GPU

$11.21/hr

Providers

Instance shapes

Provider	Instance	GPUs	vCPU	RAM	Disk	$/hr	$/hr per GPU
Spheron Network	8x B200_CC_SXM6	8×	240	1360 GB	500 GB	$89.65/hr	$11.21/hr	Launch ↗

Looking for the cheapest rate?

Hyperscaler bundles include managed networking + SLAs. Raw per-GPU rental on P2P marketplaces is typically 3–10× cheaper. See raw rental rates on the Overview tab →

AI models

Models that run on this GPU.

Các mô hình mở trọng số chạy được trên card này để suy luận, với số card ít nhất và độ chính xác cần để tải mỗi mô hình. Tính hằng ngày từ kích thước mô hình và bộ nhớ của card. Hiển thị 60 mô hình lớn nhất trong 201 mô hình chạy được.

Renting for inference?

Pair these models with the cheapest provider on the rental table. See rental rates on the Overview tab →

FAQ

AI models on this GPU.

What AI models can I run on a Nvidia B200 CC SXM6?

The list above shows open-weights models whose memory need fits this card: on one card when the model uses at least a quarter of its 180 GB, or on 2, 4 or 8 of them when it is too large for one. Each row gives the fewest cards and the precision (FP16, FP8 or INT4) that load it.

What's the VRAM minimum to run a model on the Nvidia B200 CC SXM6?

We count the weights (parameters × bytes per weight: FP16 = 2, FP8 = 1, INT4 = 0.5), add the KV cache for an 8K-token context, and add 15% for the runtime. A model fits when that is within 90% of the cards' memory. A 70B model needs about 164 GB at FP16 and about 43 GB at INT4. A longer context needs more.

How does quantization (FP16 vs FP8 vs INT4) affect what fits?

Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.

Can I fine-tune on the Nvidia B200 CC SXM6 or only do inference?

The rows here are for inference. Full fine-tuning needs several times more memory than inference at the same model size, because gradients, optimizer state and activations all live in memory. LoRA and QLoRA cut that a lot. The fine-tune cost tool sizes a run for you.

How are these GPU counts worked out?

We compute them every day from the model's parameter count and the card's memory, with the formula above. They are estimates, not tests we ran. Tokens per second, where shown, is an estimate from the card's memory bandwidth on one card.

Thuê Nvidia B200 CC SXM6.

Daily median across providers.

All providers carrying this GPU.

Run these on this GPU.

Hyperscaler bundles.

Pre-configured instances on hyperscalers.

Models that run on this GPU.

Meituan: LongCat 2.0

DeepSeek: DeepSeek V4 Pro 0813

DeepSeek: DeepSeek V4 Pro

MoonshotAI: Kimi K2.7 Code

inclusionAI: Ling-2.6-1T

inclusionAI: Ring-2.6-1T

Xiaomi: MiMo-V2.6-Pro

Xiaomi: MiMo-V2.5-Pro

Kimi K2.6

MoonshotAI: Kimi K2 0905

Kimi K2.5

Kimi K2 Thinking

Kimi K2

Inkling FP4

Meta: Muse Spark Open

Tencent: Hy4 preview

DeepSeek: DeepSeek V4.1 Flash 1017

DeepSeek: DeepSeek V4.1 Flash

GLM-5

GLM-5.1

glm-5.3

Z.ai: GLM 5.2

DeepSeek: DeepSeek V3.2

DeepSeek: DeepSeek V3.1 Terminus

DeepSeek: DeepSeek V3 0324

DeepSeek: DeepSeek V3.2 Speciale

DeepSeek-V3.1

Nex AGI: DeepSeek V3.1 Nex N1

DeepSeek: DeepSeek V3.2 Exp

DeepSeek V3

DeepSeek: R1 0528

Deep Cogito: Cogito v2.1 671B

DeepSeek R1

StepFun: Step 5 Preview

NVIDIA: Nemotron 3 Ultra

Reflection: Beam 1.0 501B A23B

Qwen: Qwen3 Coder 480B A35B

Minimax M1 80K

MiniMax-Text-01

MiniMax: MiniMax M1

MiniMax: MiniMax M3

Baidu: ERNIE 4.5 VL 424B A47B

Nous: Hermes 3 405B Instruct

Nous: Hermes 4 405B

Llama 3.1 405B

Meta: Llama 4 Maverick

Arcee AI: Trinity Large Thinking

Qwen: Qwen3.5 397B A17B

Nex AGI: Nex-N2-Pro

Nex AGI: Nex-N2.5-Pro

Hunyuan-Large

GLM-4.7

GLM-4.6

GLM-4.5

Z.ai: GLM 5.3 Flash

Xiaomi: MiMo-V2.5

Xiaomi: MiMo-V2.6-Flash

Xiaomi: MiMo-V2-Flash

DeepSeek: DeepSeek V4 Flash Vision Exp

DeepSeek: DeepSeek V4 Flash 0731

AI models on this GPU.