Accelerate AI Inference Performance by Reusing KV Cache
Software-defined AI-native data pipeline orchestrator to preserve and reuse KV tensors
No recompute - no GPU waste!
Inviting AI-Native Inference Platforms & Ne0-scalers
Our Vision
Who We Serve
The Problem
At TensorMem, we envision AI inference infrastructure operating at peak performance through software alone.
No new hardware. Just smarter systems.
The Problem
Who We Serve
The Problem
AI infrastructure pours millions into GPUs, yet much of that compute is wasted as GPUs repeatedly rebuild KV cache during every prefill phase instead of producing tokens.
This leads to lower GPU efficiency, reduced token throughput, and higher latency—particularly Time to First Token (TTFT).
Who We Serve
Who We Serve
Who We Serve
We enable AI-native platforms and inference neo-scalers to maximize ROI by preserving KV cache across the memory–storage tiers.
This improves token speed and reduces Time to First Token (TTFT) response time.
No new hardware—just smarter infrastructure!
Our Founders
Anand Kekre
Arvind Pande
Arvind Pande
IIT Bombay graduate, serial entrepreneur with two successful startup exits and 67 US patents. Former Chief Architect and CTO at Veritas and Arctera. Deep expertise in Distributed Software-defined Storage, Infrastructure, Cyber Resilience, and cloud-scale enterprise solutions.
Arvind Pande
Arvind Pande
Arvind Pande
IIT Kharagpur graduate, 15 US patents, and founding engineer of MapR Technologies - a pioneering distributed file system platform for large-scale analytics. Deep expertise in high-performance data infrastructure and hyper-scalable enterprise solutions.
About TensorMem
TensorMem Inc. is a Delaware C-Corp developing a software-defined data pipeline orchestration platform for AI era - powered by an AI-native distributed caching and storage layer.
As inference becomes the dominant driver of AI economics, TensorMem addresses the growing challenge of managing working memory (KV Cache) and data movement across the memory–storage hierarchy.
With deep expertise in distributed systems, storage, and resiliency, the team is building a foundational layer for efficient, scalable AI inference that works across cloud, on-prem, and hybrid environments.
