As Large Language Model (LLM) context windows push past a million tokens, the KV cache driving fast inference outgrows high-bandwidth GPU memory (HBM). Recomputing this context on the GPU creates severe bottlenecks, driving up time-to-first-token (TTFT) and capping session concurrency.
This technical report details a joint proof-of-value by Lightbits Labs and Solidigm, demonstrating how offloading and orchestrating KV cache reload over a disaggregated NVMe cluster drastically outperforms traditional GPU recomputation.
What you will learn:
- Detailed benchmark breakdowns comparing vanilla cold GPU recompute vs. Lightbits Inferra reload across context lengths.
- How predictive prefetching mitigates fan-in limits and serves tens of thousands of concurrent sessions with sub-2-second reload latencies.
- Strategies to optimize your inference data path and boost revenue per GPU node without adding hardware.
Get instant access to the benchmark results and technical architecture.