GPU NVIDIA

B200 SXM6

Architecture · 180GB · PCIe

VRAM
180GB
FP16
-
TDP
-
Hardware Price
-
Cloud from
$4.99/hr
1 providers
Cheapest at Lambda Labs →

Quick Insights

Performance/Dollar
N/A
FP16 performance per $1000
VRAM/Dollar
N/A
VRAM per $1000
vs null Average
N/A
FP16 TFLOPS comparison
Cloud Availability
1 providers
from $4.99/hr

Specifications

VRAM 180GB
Memory Bandwidth -
FP16 TFLOPS -
Tensor TFLOPS -
FP32 TFLOPS -
TDP -
Form Factor -
Architecture -
NVLink No
Release Date -

Cloud GPU Pricing

Rent B200 SXM6 from 1 cloud providers. Prices shown per GPU per hour.

Provider Type Instance GPUs On-Demand Per GPU Spot Availability
Lambda Labs gpu-cloud lambda-b200-sxm6 1x $4.99/hr $4.99/hr Cheapest - -

B200 SXM6 vs Alternatives

Compare B200 SXM6 with similar GPUs from other brands.

GPU VRAM FP16 TFLOPS Bandwidth Hardware Price Cloud Price
B200 SXM6 Current 180GB - - - - -
MI300X AMD 192GB (+7%) 653.7 5.3 TB/s $18k $1.99/hr Compare
MI300 AMD 128GB (-29%) 490.3 5.3 TB/s $15k - Compare
AMD Instinct MI300A AMD 128GB (-29%) 980.0 5.3 TB/s - - Compare
AMD Instinct MI250X AMD 128GB (-29%) 383.0 3.3 TB/s - - Compare
AMD Instinct MI250 AMD 128GB (-29%) 362.0 3.3 TB/s - - Compare

Best Use Cases

No specific use case recommendations for B200 SXM6 yet.

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Frequently Asked Questions about B200 SXM6

Pricing for B200 SXM6 varies. Check our cloud pricing section for rental options starting at $4.99/hr.

Yes, the B200 SXM6 with 180GB VRAM is suitable for many AI/ML workloads. For large language models, you may need multiple GPUs or consider higher-VRAM options like A100 or H100.

Consider buying for long-term, heavy usage (>4 hrs/day). Rent from cloud providers for short-term projects, experimentation, or when you need to scale quickly.

With 180GB VRAM and - FP16 TFLOPS, the B200 SXM6 can run: Large language models (7B-13B), Stable Diffusion XL, video AI, and professional 3D rendering.

The B200 SXM6 offers 180GB VRAM and - FP16 performance at its price point. Compare with similar GPUs using our comparison tool above. Key factors: VRAM for model size, TFLOPS for speed, and price for budget.