TensorMem provides a software‑defined data pipeline that captures and reuses key‑value (KV) tensors generated during the prefill phase of AI inference, eliminating redundant recomputation on GPUs.
Funding
Funding not disclosed
Founders
Product
Problem
AI inference workloads repeatedly rebuild key‑value (KV) caches during each prefill phase, causing unnecessary GPU compute, lower efficiency, reduced token throughput, and higher latency, especially for the time‑to‑first‑token metric.
Solution
TensorMem offers a software‑defined data pipeline orchestrator that captures KV tensors generated during inference and reuses them across subsequent token generation steps. By preserving these caches in memory‑storage tiers, the platform eliminates redundant recomputation, freeing GPU cycles for actual model work. This approach improves overall GPU utilization, lowers inference latency, and increases token throughput without requiring additional hardware. The solution integrates with AI‑native inference platforms, allowing them to achieve higher performance and better return on existing GPU investments.
Target Audience
Primary customers are AI‑native inference platform providers and large‑scale inference “neo‑scalers” that need to maximize GPU efficiency and reduce latency for high‑throughput token generation.
Features
- Software‑defined pipeline that intercepts and stores KV tensors during the prefill stage
- Automatic reuse of cached KV tensors for subsequent token generation, avoiding recompute
- Compatibility layer for AI‑native inference platforms and neo‑scaler architectures
- Memory‑storage tier management to balance cache persistence and GPU bandwidth
- Transparent integration that requires no changes to model code or hardware configuration