Frontier AI and HPC
NVIDIA B200
Blackwell · 180 GB SXM
Memory
180 GB HBM3e
180 GB HBM3e
Bandwidth
8 TB/s
8 TB/s
FP8 Tensor performance
9 PFLOPS*
9 PFLOPS*
Interconnect
1.8 TB/s NVLink
1.8 TB/s NVLink
Independently verified GPU comparison Updated July 15, 2025
NVIDIA B200 vs L40S
Compare architecture, memory, AI performance, scalability, graphics capabilities and current AceCloud availability using NVIDIA’s official specifications.
Cost-efficient AI and graphics
NVIDIA L40S
Ada Lovelace · 48 GB PCIe
Memory
48 GB GDDR6 ECC
48 GB GDDR6 ECC
Bandwidth
864 GB/s
864 GB/s
FP8 Tensor performance
1,466 TFLOPS*
1,466 TFLOPS*
Maximum power
350W
350W
The honest take: Choose B200 for frontier-model training, large-model inference, high concurrency, HPC and communication-intensive multi-GPU workloads. Choose L40S when the workload fits within 48 GB and requires cost-efficient inference, rendering, video or virtual workstations. B200 is unnecessary when its memory, bandwidth and NVLink capabilities remain underused.
Quick answer
Which GPU fits your workload?
A workload-first view of where B200 unlocks AI scale and where L40S avoids unnecessary infrastructure cost.
Frontier-model training
Large-scale pretraining, full fine-tuning and distributed AI
B200 B200 provides native FP4, 180 GB HBM3e, fifth-generation Tensor Cores and high-bandwidth NVLink. It is designed for demanding training workloads operating across multiple GPUs.
Smaller and mid-sized inference
Production AI services fitting comfortably within 48 GB
L40S L40S provides strong FP8 inference performance without requiring a frontier-scale platform. It is better aligned with cost-controlled production deployments.
Large-model inference
70B+ models, long contexts and high request concurrency
B200 Its larger memory and 8 TB/s bandwidth provide substantially more room for model weights, KV cache, batching and runtime overhead.
Rendering and visual computing
Ray tracing, Omniverse, video and professional visualization
L40S L40S includes RT Cores, display outputs and dedicated video encoders. B200 is designed for AI and HPC rather than professional graphics.
Shared AI infrastructure
Hardware-isolated workloads across multiple users or services
B200 B200 supports up to seven MIG instances. L40S supports vGPU software but does not support hardware-level MIG partitioning.
PoC and controlled budget
Experimentation, fine-tuning and early-stage AI deployment
L40S L40S is currently available with transparent AceCloud pricing. B200 capacity is waitlist-based and should be reserved for workloads that can use its additional scale.
Side by side
Full technical specifications
Every technical figure below is based on NVIDIA’s official B200 and L40S specifications.
NVIDIA B200 and NVIDIA L40S technical specification comparison
| Specification | NVIDIA B200 | NVIDIA L40S |
|---|---|---|
| Architecture | Blackwell | Ada Lovelace |
| Typical deployment | HGX/DGX SXM systems | PCIe data-center servers |
| GPU memory | 180 GB HBM3e | 48 GB GDDR6 ECC |
| Memory bandwidth | 8 TB/s | 864 GB/s |
| Tensor Cores | Fifth generation | Fourth generation |
| Transformer Engine | Second generation | First generation |
| FP4 Tensor, sparse | 18 PFLOPS | Not supported |
| FP4 Tensor, dense | 9 PFLOPS | Not supported |
| FP8 Tensor, sparse | 9 PFLOPS | 1.466 PFLOPS |
| FP8 Tensor, dense | 4.5 PFLOPS | 733 TFLOPS |
| FP16/BF16 Tensor, sparse | 4.5 PFLOPS | 733 TFLOPS |
| FP16/BF16 Tensor, dense | 2.25 PFLOPS | 362.05 TFLOPS |
| TF32 Tensor, sparse | 2.25 PFLOPS | 366 TFLOPS |
| FP32 | 75 TFLOPS | 91.6 TFLOPS |
| FP64/FP64 Tensor | Approximately 37 TFLOPS | Not listed |
| RT Cores | Not listed | 142 third-generation |
| RT Core performance | Not listed | 212 TFLOPS |
| Video encoders | Not listed | 3 NVENC |
| Video decoders | Supported | 3 NVDEC |
| AV1 encoding | Not listed | Supported |
| Display outputs | None listed | 4× DisplayPort 1.4a |
| MIG support | Up to 7 instances | No |
| NVLink | Fifth generation, 1.8 TB/s per GPU | No |
| NVSwitch | Supported | No |
| Secure/confidential computing | Supported | Secure boot with root of trust |
| Form factor | SXM in HGX/DGX systems | Dual-slot PCIe |
| AceCloud availability | Waitlist access | Currently available |
| AceCloud configurations | No public configuration table | 1×, 2× and 4× GPUs |
| Public AceCloud price | Not listed | From ₹83,000/month |
A30 costs approximately 2.6× more per month, but delivers over 9× the theoretical FP16 and INT8 Tensor performance with sparsity. If the workload can use that additional throughput, A30 may provide a lower cost per completed job or inference despite its higher monthly rate.
Straight comparison
How much more performance does B200 provide?
Raw specifications translated into simple relative performance. Longer bars indicate higher output.
GPU memory
GB L40S
B200
Memory bandwidth
GB/s L40S
B200
Sparse FP8 Tensor performance
PFLOPS L40S
B200
Sparse FP16/BF16 Tensor performance
TFLOPS L40S
B200
FP32 performance
TFLOPS B200
L40S
GPU-to-GPU communication
TB/s NVLink per GPU L40S
B200
Disclaimer: These are theoretical specifications. Do not convert these ratios directly into training-time or inference-throughput claims. Performance depends on precision, model architecture, framework, batch size, sparsity utilization and system configuration.
What you pay
NVIDIA B200 vs L40S pricing and availability
Current entry-level Noida pricing for Linux instances, excluding taxes:
NVIDIA L40S 48 GB
Current AceCloud pricing:
- 1× L40S
- 16 vCPUs
- 64 GB RAM
- ₹83,000/month
NVIDIA B200 180 GB
AceCloud currently indicates:
- B200 waitlist access
- No dedicated public pricing page
- No public AceCloud configuration table
A30 costs approximately 2.6× more per month, but delivers over 9× the theoretical FP16 and INT8 Tensor performance with sparsity. If the workload can use that additional throughput, A30 may provide a lower cost per completed job or inference despite its higher monthly rate.
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