How much will Accelova save you?
A 30-second estimate from your workload.
1 · WHAT'S YOUR WORKLOAD?
2 · YOUR CLUSTER
Model you serve
Your GPU rack type
Peak request rate 1,000 req/s
Steady-state inference requests your cluster needs to serve
Cache hit rate with Accelova 80%
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.