Google TPU v6 Trillium
0
CUDA Cores
32GB
VRAM
1200
GB/s
Technical Specifications
0
CUDA Cores
0
Base MHz
0
Boost MHz
32GB HBM3
16384-bit bus
Performance
500
FP32 TFLOPS
1000
FP16 TFLOPS
300W
TDP
Cloud Availability
0
Available Instances
$0.00/hr
Starting Price
Detailed Specifications
| Architecture | TPU v6 (Unknown) |
| Release Date | 2024-05-14 |
| Launch Price | $2,000.00 |
| Process | 4nm |
| Transistors | Unknown |
AI Features
v6
Tensor Cores
Disabled
Transformer Engine
Supported
Flash Attention
About TPU v6 Trillium GPU
The Google TPU v6 Trillium is a powerful GPU designed for AI/ML workloads, offering exceptional performance for both training and inference tasks. With 32GB 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 v6 Trillium features TPU v6 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 v6 Trillium, 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 1200 GB/s enabling fast data transfer for large datasets.
The TPU v6 Trillium features CUDA compute capability the latest and is compatible with all major deep learning frameworks including PyTorch, TensorFlow, and JAX. Its 4nm 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 v6 Trillium
Complete Specifications for the Google TPU v6 Trillium
Get detailed technical specifications for the Google TPU v6 Trillium 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 v6 Trillium Cloud Rental Prices per Hour
Find the best cloud rental prices for the Google TPU v6 Trillium 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 v6 Trillium the Right GPU for Your AI Workload?
Learn whether the Google TPU v6 Trillium 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.