Specifications Compared
| Spec | GB300 | V100 |
|---|---|---|
| TDP | 1400W | 300W |
| VRAM | 288 GB | 16-32 GB |
| Memory Type | HBM3e | HBM2 |
| Architecture | Blackwell Ultra | Volta |
| Form Factors | SXM | SXM2, PCIe |
| Interconnect | NVSwitch, NVLink | NVLink, PCIe 3.0 |
| FP8 Performance | 4,500 TFLOPS | |
| FP16 Performance | 2,250 TFLOPS | 125 TFLOPS |
| FP32 Performance | 90 TFLOPS | 15.7 TFLOPS |
| FP64 Performance | 45 TFLOPS | 7.8 TFLOPS |
| INT8 Performance | 4,500 TOPS | |
| Memory Bandwidth | 12,000 GB/s | 900 GB/s |
Performance Analysis
The GB300's FP16 performance of 2250 TFLOPS vastly outpaces the V100's 125 TFLOPS, enabling 18 times faster matrix multiplications essential for deep learning training. FP32 throughput at 90 TFLOPS on the GB300, versus 15.7 TFLOPS on the V100, accelerates simulations and precision-bound tasks by over 5.7 times. FP8 capability on the GB300 reaches 4500 TFLOPS, ideal for efficient inference on quantized models.
Memory bandwidth differences prove critical: the GB300's 12000 GB/s supports batch sizes up to 13 times larger than the V100's 900 GB/s limit, reducing data loading bottlenecks in large language model training. The 288 GB VRAM on the GB300 accommodates models exceeding 100 billion parameters without partitioning, unlike the V100's 16 GB constraint that forces model parallelism or smaller batches. Power draw at 1400W TDP for the GB300 demands robust cooling, contrasting the V100's efficient 300W.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
Tesla V100 16GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 TensorDock | NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 0 vCPU 0GB RAM | Texas | $0.19/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 0 vCPU 0GB RAM | New York City | $0.19/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | Texas | $0.29/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | New York City | $0.29/GPU/hr | Available | ||
 Lambda Labs | 8×NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 88 vCPU 448GB RAM 6041GB Storage | Texas | $0.79/GPU/hr $6.32/hr total (8×) | Available |
When to Choose the GB300 SXM6
Opt for the NVIDIA GB300 SXM6 in large-scale AI training where 288 GB HBM3e VRAM fits trillion-parameter models intact. Its 2250 TFLOPS FP16 performance handles hyperscale clusters via NVSwitch and NVLink interconnects. Inference benefits from 4500 TFLOPS FP8 for high-throughput quantized deployments.
Scenarios include research labs pushing state-of-the-art LLMs, as no live cloud offers exist yet, positioning it for enterprise on-premises or future availability.
When to Choose the Tesla V100 16GB
The NVIDIA Tesla V100 16GB suits budget-constrained prototyping with cloud pricing from $0.10 per hour. Its 300W TDP and PCIe/SXM2 form factors integrate easily into existing clusters. Legacy codebases optimized for Volta run without modifications.
Choose it for small-scale fine-tuning or inference on models under 7 billion parameters, leveraging 24 live offers averaging $0.82 per hour.
Use Cases
The GB300's 288 GB HBM3e VRAM and 2250 TFLOPS FP16 support trillion-parameter models without sharding. V100's 16 GB limits scale to smaller datasets only.
GB300 FP8 at 4500 TFLOPS enables high-throughput serving of quantized models. Its 12000 GB/s bandwidth handles large batches efficiently.
GB300 accommodates full model loading with 288 GB VRAM for efficient fine-tuning. V100 requires gradient checkpointing due to 16 GB constraint.
GB300's 90 TFLOPS FP32 and vast memory accelerate diffusion model generation at high resolutions. V100 struggles with memory for large latents.
V100's 15.7 TFLOPS FP32 suffices for many simulations at $0.10 per hour. GB300's 1400W TDP overkill for non-AI HPC tasks.
Frequently Asked Questions
What is the VRAM difference between GB300 SXM6 and V100 16GB?▾
The GB300 SXM6 offers 288 GB HBM3e VRAM, while the V100 16GB provides 16 GB HBM2. This 18-fold increase allows the GB300 to handle massive models without offloading. V100 suits smaller workloads fitting within 16 GB.
How does memory bandwidth compare?▾
GB300 SXM6 achieves 12000 GB/s, over 13 times the V100's 900 GB/s. Higher bandwidth on GB300 supports larger batch sizes in training. V100 bandwidth limits throughput for data-intensive tasks.
What are the FP16 performance specs?▾
GB300 delivers 2250 TFLOPS FP16, compared to V100's 125 TFLOPS. This yields approximately 18 times faster half-precision compute for AI training on GB300. V100 remains viable for legacy half-precision jobs.
Is cloud pricing available for these GPUs?▾
V100 16GB starts at $0.10 per hour, averaging $0.82 across 24 offers. GB300 has no live cloud offers currently. V100 provides immediate budget access.
What is the power consumption difference?▾
GB300 SXM6 TDP is 1400W, versus V100's 300W. GB300 requires advanced cooling for dense deployments. V100 offers lower operational costs in power-limited setups.
Which interconnects do they use?▾
GB300 employs NVSwitch and NVLink for multi-GPU scaling. V100 uses NVLink and PCIe 3.0. GB300 excels in large clusters; V100 fits smaller nodes.
Which is cheaper to rent, the GB300 or the V100?▾
Cloud rental prices for both the GB300 and V100 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 GB300 have compared to the V100?▾
The GB300 has 288 GB of HBM3e memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find GB300 and V100 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 GB300 and the V100?▾
The GB300 uses the Blackwell Ultra architecture (2025) while the V100 uses Volta (2017). The GB300 delivers 18.0x the FP16 throughput and 13.3x the memory bandwidth of the V100.