Smart KV cache orchestration engine

Predictive Prefetch. Superior Tokenomics.

Inference economics start with better tokenomics not bigger budgets. Inferra replaces reactive page-faults with predictive prefetch, solving GPU stalls waiting on memory, not compute.

KV cache engine prefetching between HBM, DRAM and storage
vLLM SGLang TensorRT
>100X TTFT acceleration
16X Multi-tenant density
10M+ Token Context
The problem

Every GPU hits the same memory wall

Static model weights already eat 35-70% of HBM. KV cache grows linearly with every token and outgrows remaining capacity.

GPU HBMweights + hot KV working set
Static weights Hot KV working set
KV cache demand1M to 10M+ tokens of context
Static weights KV cache NVMe tier via LightInferraHBM performance at NVMe cost · 50× native KV capacity
Static weights, unchanged KV cache, tiered across HBM and NVMe Ceiling no longer binding
The fix

Predictive fetch instead of reactive fetch

Conventional systems fetch missing cache after the GPU to stall. Inferra prefetches in sub-linear time eliminating GPU stalls.

Without Inferra

Reactive fetch

0 1 2 3 4 5 6 ?

GPU stalls at every page-fault

With Inferra

Proactive prefetch

0 1 2 3 4 5 6 7 8 9 10 11

Sub-linear prefetch – no stalls

Inferra by Lightbits — Inference Economics: What does a million tokens cost on your fleet?
Business impact

Idle GPUs are a margin problem, not just a
performance one

Modeled on an H200 SXM 8x cluster hosting Llama-4-405B workloads. Figures are illustrative.

GPU utilization

Before
With Inferra
0% 100%

Revenue per node / day

Before
With Inferra
$0 $500

Resources to Get You Started

SOLUTION BRIEF

Inferra – an Intelligent KV Cache Orchestration Engine
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VIDEO

Inferra Introduction
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TECH PAPER

Inferra Optimized AI Inference
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BLOG

10 Million Tokens in Production. Inferra Breaks The GPU Memory Wall.
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BLOG

Improved AI Token Economy
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