Use-case guide · Summarisation

Should you pick Groq LPU for summarisation?

Groq LPU has 230 GB VRAM. Whether it's the right fit for summarisation depends on your model size, expected QPS, and budget. Below is what we're seeing in production.

VRAM + model fit

Groq LPU fits models up to ~161B parameters in BF16 comfortably with room for KV-cache. For summarisation specifically, you'll want to leave headroom for context length growth.

Pricing

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

Throughput

On summarisation workloads, Groq LPU 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.