Specifications Compared
| Spec | B200 | T4 |
|---|---|---|
| TDP | 1000W | 70W |
| VRAM | 192 GB | 16 GB |
| CUDA Cores | 18,432 | 2,560 |
| Memory Type | HBM3e | GDDR6 |
| Architecture | Blackwell | Turing |
| Form Factors | SXM, NVL | PCIe |
| Interconnect | NVLink, PCIe 6.0, InfiniBand | |
| Tensor Cores | 576 | 320 |
| FP8 Performance | 9,000 TFLOPS | |
| FP16 Performance | 4,500 TFLOPS | 8.1 TFLOPS |
| FP32 Performance | 90 TFLOPS | 8.1 TFLOPS |
| FP64 Performance | 45 TFLOPS | |
| INT8 Performance | 9,000 TOPS | 130 TOPS |
| Memory Bandwidth | 8,000 GB/s | 320 GB/s |
Performance Analysis
FP16 performance defines training efficiency: B200 achieves 4500 TFLOPS, enabling rapid iteration on large language models, while T4's 8.1 TFLOPS suits only modest datasets. FP32 at 90 TFLOPS on B200 supports precise scientific computations, exceeding T4's 8.1 TFLOPS. The B200's FP16 dominance aids mixed-precision training, cutting memory demands by half without sacrificing accuracy.
Memory specifications dictate batch size feasibility: 192 GB VRAM on B200 accommodates batches 12 times larger than T4's 16 GB limit, ideal for stable gradient flows in deep learning. 8000 GB/s bandwidth on B200 prevents data starvation during inference, unlike T4's 320 GB/s which constrains high-throughput serving. FP8 at 9000 TFLOPS on B200 further accelerates quantized inference for real-time applications.
Power draw reflects capability: B200's 1000W TDP powers sustained peaks, contrasting T4's efficient 70W for always-on tasks.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
B200 NVL
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Nebius | NVIDIA B200 SXM 192GB VRAM | 192GB | 20 vCPU 224GB RAM | 🌍Europe | $3.95/GPU/hr | |||
 Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $4.79/GPU/hr $38.32/hr total (8×) | |||
Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $5.39/GPU/hr $43.12/hr total (8×) | |||
Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $5.69/GPU/hr $45.52/hr total (8×) | |||
 RunPod | NVIDIA B200 SXM 192GB VRAM | 192GB | 28 vCPU 283GB RAM | California | $5.89/GPU/hr |
Tesla T4
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 AWS | NVIDIA Tesla T4 16GB VRAM | 16GB | 4 vCPU 16GB RAM | Virginia | $0.53/GPU/hr | |||
AWS | NVIDIA Tesla T4 16GB VRAM | 16GB | 8 vCPU 32GB RAM | Virginia | $0.75/GPU/hr | |||
AWS | 4×NVIDIA Tesla T4 16GB VRAM | 16GB | 48 vCPU 192GB RAM | Virginia | $0.98/GPU/hr $3.91/hr total (4×) | |||
AWS | NVIDIA Tesla T4 16GB VRAM | 16GB | 16 vCPU 64GB RAM | Virginia | $1.20/GPU/hr | |||
AWS | NVIDIA Tesla T4 16GB VRAM | 16GB | 32 vCPU 128GB RAM | Virginia | $2.18/GPU/hr |
When to Choose the B200 NVL
Opt for the B200 in large-scale LLM training or fine-tuning: 4500 TFLOPS FP16 and 192 GB VRAM process billion-parameter models seamlessly, where T4's 8.1 TFLOPS and 16 GB fail. NVLink interconnects enable multi-GPU scaling for distributed workloads at $10.50 per hour.
Scientific simulations requiring 90 TFLOPS FP32 or high-bandwidth data flows favor B200's 8000 GB/s over T4 limitations.
When to Choose the Tesla T4
Choose the T4 for cost-sensitive inference on small models: 8.1 TFLOPS FP16 delivers adequate serving at $0.53 per hour starting price, far below B200's $10.50. 70W TDP suits dense cloud or edge clusters without cooling overheads.
Legacy applications or prototyping benefit from PCIe form factor and low power, avoiding B200's 1000W demands.
Use Cases
B200's 4500 TFLOPS FP16 and 192 GB VRAM manage massive datasets for billion-parameter models. T4's 8.1 TFLOPS and 16 GB VRAM cannot scale adequately.
B200's 9000 TFLOPS FP8 and 8000 GB/s bandwidth enable low-latency serving of large models. T4 limits throughput with 320 GB/s.
192 GB VRAM on B200 supports extensive fine-tuning batches. T4's 16 GB restricts to small adapters.
T4 handles standard resolutions at low cost with 8.1 TFLOPS. B200 accelerates high-res or batch generation via 4500 TFLOPS FP16.
B200's 90 TFLOPS FP32 excels in simulations. T4's matching 8.1 TFLOPS FP32 falls short for complex datasets.
Frequently Asked Questions
What is the VRAM difference between NVIDIA B200 and T4?▾
B200 offers 192 GB HBM3e VRAM, while T4 provides 16 GB GDDR6. This 12-fold increase allows B200 to load models over ten times larger without swapping.
How do FP16 performances compare on B200 vs T4?▾
B200 delivers 4500 TFLOPS FP16, surpassing T4's 8.1 TFLOPS by over 550 times. Such disparity accelerates AI training dramatically on B200.
What are the cloud pricing differences for B200 NVL and T4?▾
B200 NVL averages $10.50 per hour from one offer, compared to T4's $0.53 per hour start and $1.66 average across six offers. T4 provides budget entry for inference.
Does B200 or T4 have higher memory bandwidth?▾
B200 achieves 8000 GB/s, 25 times T4's 320 GB/s. Higher bandwidth on B200 reduces bottlenecks in data-heavy tasks.
What are the TDP ratings for B200 and T4?▾
B200 requires 1000W TDP for peak performance, versus T4's efficient 70W. T4 suits power-constrained environments.
Can T4 handle LLM inference like B200?▾
T4's 16 GB VRAM limits it to small LLMs at 8.1 TFLOPS FP16. B200's 192 GB and 4500 TFLOPS FP16 serve large models scalably.
Which is cheaper to rent, the B200 or the T4?▾
Cloud rental prices for both the B200 and T4 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the B200 have compared to the T4?▾
The B200 has 192 GB of HBM3e memory. The T4 has 16 GB of GDDR6 memory.
Can I find B200 and T4 GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the B200 and the T4?▾
The B200 uses the Blackwell architecture (2024) while the T4 uses Turing (2018). The B200 delivers 555.6x the FP16 throughput and 25.0x the memory bandwidth of the T4.