Deep dive on LLM Inference at Scale — Harshul Jain, Audible & Tanmay Sah, Independent AI Researcher

AI Engineer · 2026-09-08

Deep dive on LLM Inference at Scale — Harshul Jain, Audible & Tanmay Sah, Independent AI ResearcherAI Engineer ▶ Play the video

Why it earned a slot

Memory bottlenecks in LLM inference are precise math. Jain and Sah illustrate this by showing that a single token of KV cache on Mistral 7B costs 131 KB, meaning 16,000 tokens for 80 concurrent users requires 42 GB of GPU memory—far exceeding the capacity of a 24 GB card. I'd argue that understanding this specific relationship between context length and concurrent users is the first step in diagnosing why requests fail on consumer-grade hardware.

The short version

The brutal math of the KV cache

Why it matters

When memory climbs with context length and throughput collapses, it's usually because the server is handling requests naively or the cache is starving the GPU.

My take

I think the transition from multi-head to grouped query or latent attention is a critical lever for managing the memory climb associated with context length, though the actual impact would depend on the specific concurrency requirements of a given workload.

How it connects

• Compare the throughput of naive sequential processing against batching on your own workload.
• Test if grouped query attention reduces your memory ceiling compared to standard multi-head attention.

Bottom line

Calculate your expected KV cache footprint (tokens * users * cost per token) before selecting your GPU hardware.