How much will Accelova save you?

A 30-second estimate from your workload.

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
3-YEAR INFRASTRUCTURE SAVINGS
$0
on GPU CapEx, net of appliance cost
———

Pick your cluster setup above to see the estimate.

Why these numbers are honest
  • Drop-in, no workload changes. Accelova is modeled as a transparent appliance layered in front of your existing GPU racks. You install hardware + plugin; vLLM / SGLang / TRT-LLM keep running unchanged.
  • Baseline assumes you're already running vLLM with prefix caching. The "without Accelova" comparison uses a 15% baseline hit rate (typical for cold-prefix / single-turn workloads). If your workload already gets more local cache benefit, the savings shown here are conservative.
  • Speedup is derived, not declared. TTFT and throughput come from a physical model of your hit rate × model × rack — attention FLOPs, MFU curve, HBM bandwidth, PCIe ceiling. Not a marketing slider.
  • Net of appliance cost. Savings are 3-year amortized GPU CapEx minus 3-year amortized appliance CapEx + power + cooling. We don't hide the cost of the box we're selling.
PROCUREMENT-READY ANALYSIS
Want a custom TCO report for your deployment?
We'll model your specific models, traffic patterns, and infrastructure — with our solutions team — and send you a written analysis your finance team can sign off on.