Google TPU v7 Ironwood
0
CUDA Cores
192GB
VRAM
7370
GB/s
Technical Specifications
0
CUDA Cores
0
Base MHz
0
Boost MHz
192GB HBM3e
24576-bit bus
Performance
2307
FP32 TFLOPS
4614
FP16 TFLOPS
400W
TDP
Cloud Availability
0
Available Instances
$0.00/hr
Starting Price
Detailed Specifications
| Architecture | TPU v7 (Unknown) |
| Release Date | 2024-01-15 |
| Launch Price | $2,000.00 |
| Process | 3nm |
| Transistors | Unknown |
AI Features
v7
Tensor Cores
Disabled
Transformer Engine
Supported
Flash Attention
Physical Specifications
Additional Specifications
liquid
Cooling
About TPU v7 Ironwood GPU
The Google TPU v7 Ironwood is a powerful GPU designed for AI/ML workloads, offering exceptional performance for both training and inference tasks. With 192GB of VRAM and 0 CUDA cores, it provides the memory capacity and computational power needed for modern deep learning models.
Released in 2024, the TPU v7 Ironwood features TPU v7 architecture with advanced AI accelerators including Tensor Cores and Transformer Engine support. This makes it ideal for large language models, computer vision tasks, and generative AI applications.
When considering cloud rental options for the TPU v7 Ironwood, pricing starts at $0.00/hour from various providers. This GPU offers excellent price-to-performance for AI training workloads, with its high memory bandwidth of 7370 GB/s enabling fast data transfer for large datasets.
The TPU v7 Ironwood features CUDA compute capability the latest and is compatible with all major deep learning frameworks including PyTorch, TensorFlow, and JAX. Its 3nm manufacturing process ensures efficient power consumption relative to performance output.
External Resources
Learn more about GPUs from these authoritative sources:
Official CUDA programming guide
NVIDIA GPU Specifications →Official NVIDIA GPU specs
TechPowerUp GPU Database →Comprehensive GPU specifications
CUDA Compute Capability Guide →GPU compute capability reference
What You Need to Know About the TPU v7 Ironwood
Complete Specifications for the Google TPU v7 Ironwood
Get detailed technical specifications for the Google TPU v7 Ironwood including VRAM capacity of , CUDA core count, Tensor Core count, memory bandwidth, and CUDA compute capability of . This GPU is designed for demanding AI training, inference, and high-performance computing workloads. Understanding these specifications helps you determine whether it is the right fit for PyTorch, TensorFlow, or custom CUDA-based applications.
Compare Google TPU v7 Ironwood Cloud Rental Prices per Hour
Find the best cloud rental prices for the Google TPU v7 Ironwood across providers like RunPod, Vast.ai, Lambda Labs, and CoreWeave. GPUvec aggregates real-time pricing data so you can compare costs per hour, find available instances, and choose the most cost-effective provider. GPU cloud pricing for this model varies by region and instance type, so comparing multiple options can save significantly on compute costs.
Is the Google TPU v7 Ironwood the Right GPU for Your AI Workload?
Learn whether the Google TPU v7 Ironwood is the right choice for your specific AI and ML workloads. We cover use cases including large language model training, fine-tuning, inference serving, computer vision, scientific computing, and rendering. Compare its specifications and pricing against other GPUs like the H100, A100, and RTX 5090 to make an informed decision for your infrastructure needs.
Top GPUs for Training and Inference
| Category | Rank 1 | Rank 2 | Rank 3 |
|---|---|---|---|
| Best for Training | NVIDIA H200 | NVIDIA H100 | NVIDIA B200 |
| Best for Inference | NVIDIA A40 | NVIDIA A100 | NVIDIA A10 |
Compare GPU specifications and cloud instances to find the best GPU for your workload.