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 was introduced in 2024 on the TPU v6 architecture. Its specification combines its vendor-specific compute units and 16,384 Tensor Cores with 32GB of HBM3, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 32GB memory capacity, the TPU v6 Trillium is best evaluated for model fine-tuning, production inference, image generation, and multi-GPU development. Always size the model, optimizer state, KV cache, and framework overhead together rather than choosing a GPU from core count alone.
GPUvec does not currently have an active hourly cloud listing for the TPU v6 Trillium. This is not a zero-dollar offer: it means no comparable provider price is available in the present dataset. Check the provider directory for availability or compare a related GPU with an active listing.
For software planning, the TPU v6 Trillium combines 1,200 GB/s of memory bandwidth, CUDA compute capability the latest, and a 4nm manufacturing process. Confirm the minimum CUDA, PyTorch, TensorFlow, or driver version required by your application before renting or purchasing hardware.
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
The Google TPU v6 Trillium uses the TPU v6 architecture and combines vendor-specific compute units with 32GB of HBM3. GPUvec records 1,200 GB/s memory bandwidth alongside architecture, release date, process node, accelerator features, and physical specifications so the page can be evaluated from source data instead of model-name assumptions.
Compare Google TPU v6 Trillium Cloud Rental Prices per Hour
GPUvec currently has no comparable active hourly listing for this model; this means pricing is unavailable, not free. Provider rates can vary by region, host configuration, billing model, and availability. Use the provider directory and related-GPU links to compare an available alternative when this exact accelerator is not listed.
Is the Google TPU v6 Trillium the Right GPU for Your AI Workload?
Start with the 32GB memory limit, then evaluate 1,200 GB/s memory bandwidth, compute features, and software compatibility for your workload. Model weights, KV cache, activations, optimizer state, and framework overhead all consume memory. Compare this page with related accelerators rather than assuming that a higher core count alone guarantees a better training or inference result.
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.