Specifications Compared
| Spec | GAUDI2 | V100 |
|---|---|---|
| TDP | 600W | 300W |
| VRAM | 96 GB | 16-32 GB |
| Memory Type | HBM2e | HBM2 |
| Architecture | Gaudi | Volta |
| Form Factors | OAM | SXM2, PCIe |
| Interconnect | Ethernet | NVLink, PCIe 3.0 |
| FP16 Performance | 420 TFLOPS | 125 TFLOPS |
| FP32 Performance | 420 TFLOPS | 15.7 TFLOPS |
| Memory Bandwidth | 2,460 GB/s | 900 GB/s |
Performance Analysis
Gaudi 2's balanced 420 TFLOPS in FP16 and FP32 suits mixed-precision training, where FP32 accumulation prevents overflow, outperforming V100's imbalanced 125 TFLOPS FP16 and 15.7 TFLOPS FP32 that limits scalar FP32 tasks. In inference, V100's tensor core FP16 excels for throughput, but Gaudi 2's higher rates and 96 GB VRAM handle larger models without splitting. Memory bandwidth of 2460 GB/s on Gaudi 2 supports bigger batch sizes than V100's 900 GB/s, reducing data loading bottlenecks in training loops. Higher TDP at 600W on Gaudi 2 reflects its density versus V100's 300W efficiency. Real-world training times shrink with Gaudi 2's specs for large language models, as 96 GB VRAM fits sequences avoiding multi-GPU complexity. Inference latency benefits from Gaudi 2's bandwidth for high-resolution inputs, though V100 suffices for smaller deployments.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
Intel Gaudi 2
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 LeaderGPU | 8×Intel Gaudi 2 96GB VRAM | 96GB | 64 vCPU 2048GB RAM 96174GB Storage | Netherlands | $0.91/GPU/hr $7.29/hr total (8×) | Available | ||
 Denvr | 8×Intel Gaudi 2 96GB VRAM | 96GB | 160 vCPU 1024GB RAM 30400GB Storage | Virginia | $1.25/GPU/hr $10.00/hr total (8×) |
Tesla V100 32GB
| 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 Intel Gaudi 2
Gaudi 2 excels in large-scale LLM training requiring over 32 GB VRAM: its 96 GB HBM2e accommodates massive models without sharding. High 2460 GB/s bandwidth enables large batch sizes, accelerating convergence at 420 TFLOPS FP16. Ethernet interconnect scales clusters cost-effectively from $0.91 per hour.
When to Choose the Tesla V100 32GB
V100 fits budget-conscious inference or fine-tuning under 32 GB VRAM limits: 125 TFLOPS FP16 handles moderate throughput at $0.29 per hour starting price. Lower 300W TDP suits power-constrained clouds, with NVLink offering low-latency multi-GPU for 46 abundant offers. Legacy codebases leverage mature CUDA ecosystem.
Use Cases
Gaudi 2's 96 GB VRAM fits large models entirely, unlike V100's 32 GB requiring sharding. 420 TFLOPS FP16 and 2460 GB/s bandwidth speed up training epochs significantly.
Gaudi 2 serves high-throughput with 420 TFLOPS FP16 and vast VRAM for batching. V100's 125 TFLOPS FP16 suffices for low-volume at lower $0.29 per hour entry cost.
96 GB HBM2e on Gaudi 2 supports parameter-efficient methods on big models. Balanced 420 TFLOPS FP32 aids precise updates over V100's weak 15.7 TFLOPS.
Gaudi 2's 2460 GB/s bandwidth accelerates diffusion steps with large latents. 96 GB VRAM enables high-resolution generations without OOM errors on V100.
V100's 15.7 TFLOPS FP32 matches many simulations better than Gaudi 2's focus. Lower 300W TDP and PCIe form factor integrate into HPC clusters affordably.
Frequently Asked Questions
How much VRAM does Gaudi 2 have compared to V100?▾
Gaudi 2 offers 96 GB HBM2e, three times the V100 32GB's 32 GB HBM2. This enables larger models or batches on Gaudi 2. V100 suits smaller workloads under 32 GB.
What is the FP16 performance difference?▾
Gaudi 2 achieves 420 TFLOPS FP16, over three times V100's 125 TFLOPS. This boosts AI training and inference speed on Gaudi 2. V100 remains viable for legacy tasks.
Which has higher memory bandwidth?▾
Gaudi 2 provides 2460 GB/s, nearly triple V100's 900 GB/s. Higher bandwidth reduces bottlenecks in data-heavy workloads. It supports larger batch sizes effectively.
What are the cloud pricing differences?▾
Gaudi 2 starts at $0.91 per hour averaging $1.08 across two offers. V100 begins at $0.29 per hour averaging $1.01 with 46 offers. Availability favors V100.
How do power consumptions compare?▾
Gaudi 2 draws 600W TDP versus V100's 300W. Higher power on Gaudi 2 correlates with dense performance. V100 aids power-limited environments.
What interconnects do they use?▾
Gaudi 2 relies on Ethernet for scaling. V100 uses NVLink or PCIe 3.0 for low-latency links. Ethernet suits cost-effective clusters on Gaudi 2.
Which is cheaper to rent, the Gaudi 2 or the V100?▾
Cloud rental prices for both the Gaudi 2 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 Gaudi 2 have compared to the V100?▾
The Gaudi 2 has 96 GB of HBM2e memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find Gaudi 2 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 Gaudi 2 and the V100?▾
The Gaudi 2 uses the Gaudi architecture (2022) while the V100 uses Volta (2017). The Gaudi 2 delivers 3.4x the FP16 throughput and 2.7x the memory bandwidth of the V100.