Model your own fleet.

Estimate what a shared KV cache tier returns on your GPUs, your workload and your latency target.

1 · WHAT'S YOUR WORKLOAD?

2 · YOUR CLUSTER

Average steady-state requests/sec. Sets rack count and batch size.
Latency objective. Tighter targets cap the batch, lowering GPU utilization and raising cost per million tokens.
Interactivity objective. Tighter targets cap decode batch size and may require more racks.
Typical 70–85% for long-context coding agents
MODELED
3-YEAR INFRASTRUCTURE SAVINGS
$0
on GPU CapEx, net of the flash tier
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Pick your cluster setup above to see the estimate.

How the model works
  • Software on hardware you source. Accelova is modeled as a shared KV cache tier alongside your existing GPU racks, on commodity NVMe and RDMA. vLLM / SGLang / TensorRT-LLM keep running unchanged; no model or application changes.
  • The baseline is node-local cache. The "without Accelova" column assumes each GPU reuses only the cache it built itself, at the baseline hit rate shown in section 2. If your workload already gets more local reuse, the savings shown here are conservative.
  • Speedup is derived, not declared. TTFT and throughput come from a physical model of hit rate × model × rack: attention FLOPs, MFU curve, HBM bandwidth, PCIe ceiling.
  • Net of the flash tier. Savings are 3-year amortized GPU CapEx minus the 3-year amortized cost of the flash-tier nodes, power and cooling.
YOUR FLEET
Walk through your scenario with us.
Copy the share link for your scenario and send it with the form. We will go through the assumptions against your workload and tell you plainly whether there is a case for your fleet.