Use-case guide · Long context

Should you pick Trainium2 for long context?

Trainium2 has 96 GB VRAM. Whether it's the right fit for long context depends on your model size, expected QPS, and budget. Below is what we're seeing in production.

VRAM + model fit

Trainium2 fits models up to ~67B parameters in BF16 comfortably with room for KV-cache. For long context specifically, you'll want to leave headroom for context length growth.

Pricing

Live pricing across all providers for Trainium2 is on the GPU detail page — click through for the sortable list.

Throughput

On long context workloads, Trainium2 typically delivers the throughput published in its FP16 spec, minus the framework overhead (vLLM ≈ 85% MFU, TGI ≈ 70%).

Try the calculator to size the hardware for your specific model, or see all GPUs on the InferenceScore leaderboard.