KVCache for Real-time AI Inference
Petabyte-Scale Persistent Storage for KVCache
With Elastic Cache Fabric, the memory for AI model inferencing extends to petabyte-scale capacity, enabling long-context LLM inference and complex AI reasoning with high efficiency.
Plug-and-Play Integration
Works effortlessly with vLLM, SGLang, TensorRT-LLM, and TGI through standardized APIs—no modifications to your code needed—abstracting framework and version differences for smooth deployment.
Fully Utilize Existing Resources at Minimal Cost
Leverages a multi-layer caching architecture to fully utilize all available storage resources—including GPU VRAM, CPU DRAM, and local NVMe SSDs—without any additional hardware investment, keeping costs low.
Faster TTFT and Higher Token Throughput
By dramatically reducing TTFT and boosting token throughput, Elastic Cache Fabric delivers inference results in microseconds, providing a seamless and responsive experience for end-users.
Maximum GPU Utilization & Minimum Per-Token Cost
By extending GPU memory to a three-tier caching architecture, Elastic Cache Fabric avoids GPU memory swapping and latency spikes. This enables larger batch sizes and higher concurrency, maximizing GPU utilization and reducing both overall and per-token costs.
Shared KVCache Pool
Built on a global, multi-tier caching architecture with intelligent scheduling, Elastic Cache Fabric creates a high-performance shared KVCache pool across multiple nodes, enabling efficient cross-node cache sharing and reuse, and maximizing GPU utilization in large-scale clusters.
Empowering AI Productivity
Across All Industries
