Stop paying twice
for the same token.
Accelova is the high-bandwidth memory tier for LLM inference. A purpose-built KV cache engine that eliminates prefill recompute so your GPUs generate tokens instead of redoing work.
Inference is no longer a compute problem. It's a memory problem.
Prefill is wasted work.
Every reloaded session, agent thread, and shared prompt recomputes the same attention state on the most expensive hardware you own.
Local SSDs are not the answer.
Single-drive bandwidth, no fault tolerance, unpredictable tail latency under real load.
DRAM doesn't scale.
DRAM is the wrong economics. 30–50× the price of NVMe per GB, and at scale it costs as much as the GPUs it's serving.
Memory at the speed of inference.
Accelova is the memory tier for LLM inference — a purpose-built KV cache engine that sits next to your GPU racks and serves cached attention state at the bandwidth modern inference clusters actually need. One Accelova node absorbs the peak KV demand of a full high-end GPU rack.
Purpose-built for inference state.
Cluster cache nodes to match your context length and concurrency, with host integration through existing vLLM and SGLang connectors. A clusterable tier sized to your context and concurrency, not bolted onto general-purpose nodes.
Performance characteristics under disclosure; request the technical brief for full numbers.
Engineered for the constraints of real inference.
Bandwidth-class capacity
Engineered to feed full GPU racks at memory-tier bandwidth, not single-server speeds.
Production-grade reliability
Fault tolerance, end-to-end data integrity, encrypted at rest by default.
Hardware-enforced multi-tenancy
Isolation guarantees that hold up under audit.
Drop-in ecosystem fit
Native integration with the open-source serving stacks your team already runs.
Works with the stack you already have.
Connects over standard RDMA fabric. No model code changes.
Three places the bandwidth math changes everything.
Inference providers
Lower marginal cost per token, especially on long-context and agentic workloads.
Enterprise AI platforms
Predictable tail latency for multi-turn assistants and RAG pipelines.
Neoclouds
A differentiated capability your customers can't get from commodity GPU rental alone.
Order-of-magnitude cheaper than recompute.
Every cached prefill is GPU time you don't have to buy. At rack scale, the math is not subtle.
We built a calculator. Bring your workload, audit our assumptions, see what the math says.
Ready to stop recomputing?
We're working with a small group of design partners ahead of general availability.