AWS Trainium3 (trn3)
0
CUDA Cores
144GB
VRAM
0
GB/s
Technical Specifications
0
CUDA Cores
0
Base MHz
0
Boost MHz
144GB HBM3e
0-bit bus
Performance
40
FP32 TFLOPS
80
FP16 TFLOPS
0W
TDP
Cloud Availability
0
Available Instances
$0.00/hr
Starting Price
Detailed Specifications
| Architecture | Trainium v3 (3nm) (Unknown) |
| Release Date | 2025-12-01 |
| Launch Price | $2,000.00 |
| Process | 3nm |
| Transistors |
AI Features
Gen 3
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
About Trainium3 (trn3) GPU
The AWS Trainium3 (trn3) was introduced in 2025 on the Trainium v3 (3nm) architecture. Its specification combines its vendor-specific compute units with 144GB of HBM3e, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 144GB memory capacity, the Trainium3 (trn3) is best evaluated for large-model training, high-batch inference, and memory-intensive scientific computing. 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 Trainium3 (trn3). 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For software planning, the Trainium3 (trn3) combines unpublished memory bandwidth, CUDA compute capability the latest, and a 3nm manufacturing process.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 Trainium3 (trn3)
Complete Specifications for the AWS Trainium3 (trn3)
The AWS Trainium3 (trn3) uses the Trainium v3 (3nm) architecture and combines vendor-specific compute units with 144GB of HBM3e. GPUvec records the published memory subsystem 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 AWS Trainium3 (trn3) 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 AWS Trainium3 (trn3) the Right GPU for Your AI Workload?
Start with the 144GB memory limit, then evaluate the published memory subsystem, 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.