Tensormesh and AMD Collaborate to Empower Fewer GPUs to Serve More ModelsTensormesh, the company pioneering caching-accelerated inference optimization for enterprise AI, today announced a collaboration with AMD through which Tensormesh KV cache solution and AMD virtual memory offering will work together to allow more models to be served on fewer GPUs while retaining high KV cache hit rates and throughput even with oversubscribed high-bandwidth memory (HBM). Tensormesh is working with AMD, leveraging its GPU technology and AMD Live Context Virtualization components, and is tested using Dell servers with 8x AMD/ATI accelerators (MI355) GPUs and Dell storage. Tensormesh integrated LMCache coordinates KV cache management.
Tensormesh Raises $20M from Investors Including AMD Ventures, CoreWeave, NVentures, Launches Tensormesh Inference to Fix AI’s Most Expensive Problem
Tensormesh, the company pioneering caching-accelerated inference optimization for enterprise AI, today announced $20 million in new funding from investors including AMD Ventures, CoreWeave, NVentures (NVIDIA’s venture capital arm), Valley Capital Partners, and Laude Ventures, extending its seed round and bringing its total funding to $24.5 million. Alongside the funding, Tensormesh is announcing the general availability of Tensormesh Inference, its flagship SaaS inference platform, which fixes enterprises’ most expensive AI problem: recomputing what GPUs have already processed.
Tensormesh Emerges From Stealth to Slash AI Inference Costs and Latency by up to 10x
Tensormesh, the company pioneering caching-accelerated inference optimization for enterprise AI, today emerged from stealth with $4.5 million in seed funding led by Laude Ventures. Tensormesh’s technology eliminates redundant computation in AI inference, reducing latency and GPU spend by up to 10x while giving enterprises full control of their data and infrastructure.
