The memory layer
for AI inference.
KV Cache builds high-performance infrastructure for LLM inference.
Reuse computation. Reduce redundant work. Scale inference further.
AI is getting smarter.
Inference is getting heavier.
As context windows grow and AI applications become more sophisticated, inference requires more computation and more memory.
Repeated work becomes expensive.
The next generation of AI infrastructure needs a better memory layer.
Larger context windows create richer AI experiences.
Longer workloads increase inference pressure.
Efficient memory management becomes critical at scale.
Cache once.
Reuse everywhere.
KV Cache stores and intelligently manages key-value cache data across the inference pipeline, reducing unnecessary recomputation and making memory available where it matters most.
following document…"
Prompt
User context enters the inference pipeline.
Prefill
Compute the initial KV cache.
KV Cache
Store and manage reusable context.
Decode
Reuse cached context for subsequent tokens.
Built for scale.
Designed for performance.
KV Cache Management
Intelligent eviction, placement and lifecycle control.
Distributed Cache
Scale cache infrastructure across nodes and availability zones.
Predictive Prefetch
Anticipate demand and load data before it is needed.
Cache Compression
Fit more useful context into available memory.
Inference Optimization
Reduce unnecessary work and maximize useful compute.
Less recomputation.
More inference.
Efficient cache management can reduce redundant computation, improve memory utilization and help inference infrastructure serve more useful work at scale.
- Shared prompt prefixes are recomputed for every request
- Attention state is discarded when a worker evicts it
- Fast memory fills up with duplicate copies
- Time to first token grows with context length
- Computed attention state is stored once and reused
- Cache moves between HBM, DRAM and NVMe tiers instead of being dropped
- Memory-aware placement keeps hot entries close to the workers
- Repeated context is served from cache, not recomputed
From chips to clusters.
One cache fabric.
KV Cache connects the fast memory, inference workers and model-serving infrastructure into a coordinated cache layer.
The result is a system designed around reuse rather than repeated work.
Infrastructure should remember what it already knows.
Avoid repeating expensive computation.
Move cache intelligently across the infrastructure stack.
Build inference systems that grow with demand.
Infrastructure your models don't have to think about.
KV Cache is designed to fit into modern inference stacks without forcing application teams to rebuild their systems.
- API-first architecture
- Observability
- Distributed operation
- Memory-aware placement
- Configurable eviction
- Production monitoring
Let's talk about your inference stack.
Tell us about your workloads, infrastructure and requirements. We reply to every enquiry within one business day.
- 01Describe your current inference setup and what you want to improve.
- 02An engineer reviews your enquiry and gets back to you directly.
- 03We arrange a technical conversation, not a sales pitch.
Already working with us and need help? Visit Support or write to support@kvcache.lv.