Use of Shared Memory to Reduce CapEx in AI Inference Scale-Out
Traditional inference architectures tightly couple memory with compute, forcing organizations to purchase GPU servers to expand memory capacity. This overprovisioning can drive millions in excess CapEx without a commensurate gain in inference performance.
Traditional inference architectures tightly couple memory with compute, forcing organizations to purchase GPU servers to expand memory capacity. This overprovisioning can drive millions in excess CapEx without a commensurate gain in inference performance.
This whitepaper presents the financial and technical case for balanced inference architectures. Benchmark testing shows that eight GPU servers paired with one Penguin Solutions MemoryAI KV Cache Server delivers up to 2× inference throughput—the performance equivalent of 16 GPU servers—while reducing CapEx by up to 35%. The "8 + 1 = 16" paradigm redefines the economics of enterprise inference at scale.
Readers Will Learn
- How to identify the signs of memory-bound inference
- Which workloads benefit most from expanded KV capacity
- How KV preservation and restore workflows work
- How external KV memory extends effective KV capacity
Key Benchmark Results Include
- Up to 2× inference throughput with one MemoryAI KV Cache Server
- Up to 35% CapEx reduction by cutting conventional GPU requirements in half
- 17.2× lower TTFT for interactive workloads
Use of Shared Memory to Reduce CapEx in AI Inference Scale-Out
Traditional inference architectures tightly couple memory with compute, forcing organizations to purchase GPU servers to expand memory capacity. This overprovisioning can drive millions in excess CapEx without a commensurate gain in inference performance.