Google TPU v5e
0
CUDA Cores
16GB
VRAM
600
GB/s
Technical Specifications
0
CUDA Cores
0
Base MHz
0
Boost MHz
16GB HBM2e
8192-bit bus
Performance
275
FP32 TFLOPS
550
FP16 TFLOPS
250W
TDP
Cloud Availability
0
Available Instances
$0.00/hr
Starting Price
Detailed Specifications
| Architecture | TPU v5e (Unknown) |
| Release Date | 2023-08-29 |
| Launch Price | $2,000.00 |
| Process | 5nm |
| Transistors | Unknown |
AI Features
v5e
Tensor Cores
Disabled
Transformer Engine
Supported
Flash Attention
About TPU v5e GPU
The Google TPU v5e was introduced in 2023 on the TPU v5e architecture. Its specification combines its vendor-specific compute units and 8,192 Tensor Cores with 16GB of HBM2e, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 16GB memory capacity, the TPU v5e is best evaluated for quantized language-model inference, image generation, and medium-sized training jobs. 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 v5e. 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 v5e combines 600 GB/s of memory bandwidth, CUDA compute capability the latest, and a 5nm 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 v5e
Complete Specifications for the Google TPU v5e
The Google TPU v5e uses the TPU v5e architecture and combines vendor-specific compute units with 16GB of HBM2e. GPUvec records 600 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 v5e 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 v5e the Right GPU for Your AI Workload?
Start with the 16GB memory limit, then evaluate 600 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.