Cut inference costs by 80% with intelligent KV-cache reuse.
Large language models spend most of their compute re-processing identical prompt prefixes — system prompts, RAG context, few-shot examples. Neural Cache intercepts these redundant computations at the attention layer and serves cached KV tensors directly, making repeated inference nearly free.
Automatically detects shared prompt prefixes across concurrent requests. Shared KV tensors are computed once and reused, eliminating redundant forward passes.
Hot prefixes live in GPU HBM for zero-copy access. Warm prefixes spill to host DRAM. Cold prefixes are evicted to NVMe with sub-millisecond re-materialisation.
Beyond exact-match prefix caching, semantic cache identifies semantically equivalent prompts using embedding similarity, further reducing compute for paraphrased queries.
No changes to your prompt or application code. Cache logic sits between your API calls and the inference engine — invisible to the client.
Per-request breakdowns show exactly how much was served from cache vs recomputed. Export to your cost management tooling via OpenTelemetry.
Pre-warm system prompt and few-shot example caches before traffic arrives. Schedule warming jobs to ensure zero cold-start latency for your most common prefixes.
| Cache granularity | Token-level KV tensor |
| Hot tier | GPU HBM (in-flight) |
| Warm tier | Host DRAM, zero-copy via NVLink |
| Cold tier | NVMe, < 1 ms re-load |
| Cost reduction | Up to 80% on repeat prefixes |
| Latency overhead | < 0.1 ms for cache hit |
| Integration | Transparent proxy, no SDK changes |
| Semantic cache | Planned — embedding-based |
Neural Cache is currently in development — estimated Q3 2026.
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