As context windows scale to millions of tokens, the KV cache grows proportionally, rapidly exhausting limited GPU HBM capacity. This forces systems to rely on frequent evictions and expensive recomputation, which inflates Time-to-First-Token (TTFT), limits session concurrency, and squanders valuable GPU cycles.
VAST Data for KV Cache
Scale Context Windows, not GPU Costs
Transform key-value (KV) cache from a bottleneck into an accelerant! Eliminate expensive GPU recomputation to slash Time-to-First-Token (TTFT) and unlock AI inference at scale.
The Inference Challenge
Costly and Limited GPU Memory Kills Agentic AI Initiatives
Inefficient GPU Usage and HBM Fragmentation
GPUs are wasting time with recompute. This high "recompute tax" on memory reduces the number of concurrent users served.
Context Truncation
Models are forced to "forget" earlier parts of a conversation to save space. This leads to severe degradation in reasoning capability, task failure, or an infinite execution loop.
Exponentially Higher Token Costs
Inefficient use of GPUs can spur the need for more GPUs to provide additional memory, even if compute power isn't the bottleneck.