AI · ML Infrastructure

Your GPUs Are Waiting for Memory.
We Eliminate the Wait.

Every LLM inference, every attention computation, every embedding lookup stalls on memory. KV cache reads from HBM cost 1-10ms. Cachee serves them from CPU L1 at 17 nanoseconds. Your GPUs stay fed. Your costs drop 40-60%.

LLM InferenceEmbedding LookupsKV CacheModel ServingRAG PipelinesAttention State

17ns

KV Cache Read

From CPU L1

3-5×

Inference Throughput

Same GPU fleet

40-60%

GPU Cost Reduction

Fewer instances needed

99.97%

Cache Hit Rate

AI-predicted prefetch

The GPU Memory Wall

Your Compute Is Fast. Your Memory Isn't.

H100 GPUs deliver 3,958 TFLOPS of compute. But GPU utilization sits at 30-40% because HBM bandwidth is the bottleneck. Every token generated, every attention head computed, every embedding retrieved stalls on memory. You're paying for compute you can't use.

⚡

The Memory Wall

GPU compute utilization sits at 30-40% because HBM bandwidth is the bottleneck. $4/hr H100s spending 60% of time waiting for memory. Thousands of TFLOPS sitting idle while the memory bus saturates on KV cache reads.

30-40% GPU util

📈

KV Cache Explosion

LLM context windows growing from 4K to 1M+ tokens. KV cache per request: 2-16GB. HBM fills up, throughput collapses. Longer contexts mean exponentially more memory pressure, and every concurrent request compounds the problem.

16GB/request

💰

Cost Spiral

More context = more memory = more GPUs. Teams buying 2-3x the GPUs they need because memory, not compute, is the constraint. The AI infrastructure bill grows linearly with context length, not model complexity.

$3.7B wasted/yr

The Transformation

From Memory-Bound to Compute-Bound. Every Inference.

Without Cachee

1-10ms

per KV cache read

GPU Utilization30-40%

Tokens/sec/GPU2,000

Cost per 1M tokens$0.06

p99 Latency850ms

→

With Cachee

17ns

per KV cache read

GPU Utilization85-95%

Tokens/sec/GPU8,000

Cost per 1M tokens$0.015

p99 Latency180ms

The Business Case

More Throughput Per GPU. Lower Latency. Massive Savings.

4× Inference Throughput

Same hardware — just eliminate the memory wall

$2.1M

Annual GPU savings
per 100 H100s

4×

Tokens per second
per GPU

75%

Reduction in
p99 latency

99.97%

KV cache
hit rate

Use Cases

From LLM Serving to Multi-Model Orchestration.

LLM Serving

Serve cached KV states for repeated prefixes. Shared system prompts, common queries, and popular contexts hit L1 instantly. Eliminate redundant KV computation for the 80% of requests that share prefix tokens. Throughput scales with cache hits, not GPU count.

RAG Pipelines

Embedding lookups and retrieval results cached at 17ns. Eliminate redundant vector DB round-trips. 10x faster retrieval for frequently accessed documents. Hot embeddings live in L1 — cold storage stays in the vector DB where it belongs.

Real-Time ML

Feature stores, model weights, and inference state at 17ns. Sub-millisecond predictions for fraud detection, recommendation engines, and dynamic pricing. Every feature lookup that hits L1 is a feature lookup that doesn't block your prediction pipeline.

Multi-Model Orchestration

Agent frameworks making 10-50 LLM calls per request. Cache intermediate results between model calls. Cut compound latency by 80%. When your agent chain makes the same sub-call twice, the second one returns in nanoseconds instead of seconds.

Your GPUs Are Waiting for Memory.We Eliminate the Wait.

Your GPUs Are Waiting for Memory.
We Eliminate the Wait.