AI agents for generative AI infrastructure
Choose your model, hardware, and workload targets. ProsGrow agents benchmark, optimize, and deploy your inference stack for your performance and cost goals. Use your infrastructure or get ready compute from ProsGrow.
Your workload. Your quality, latency, and memory constraints. Measured results.
Completed benchmarks
Measured results across video, speech, language, image generation, retrieval, and document AI on NVIDIA Blackwell.
Solutions
Our agents tune the inference stack around your workload: more throughput, lower latency, lower serving cost, or higher concurrency, within your quality and memory constraints.
Granite’s queued audio API reached 28,368.99 RTFx—48.82% more throughput after CPU-affinity changes and 16-file requests. RTFx is audio seconds processed per wall second; live concurrency needs a separate evaluation.
Align model efficiency, batching, routing, and serving with the traffic your application needs to handle.
Our document QA test reached 45,978 questions/hour at 96.53% ANLS—3.07% more throughput after shard balancing on the same eight GPUs. ANLS measures answer similarity across 5,349 DocVQA questions.
Agents, compute & inference
Bring your model, workload, and hardware requirements. ProsGrow agents handle inference optimization and deployment on your infrastructure or with compute supplied by us.
Our agents benchmark your workload and tune runtime, precision, batching, caching, and parallelism, then deploy the selected configuration against your performance and quality targets.
Get compute, agent-driven optimization, and managed inference together, delivered through APIs, dedicated endpoints, or private deployments.
One package across the stack
Our data center partners can provide 50+ MW of power capacity with B300 or other accelerators, ready in 3 months.
Deployment
Define where inference runs, how your application connects, and who operates each part of the system.
Plan inference in your private environment around data access, network controls, and existing infrastructure.
Size dedicated GPU resources against your throughput, latency, and quality targets, with a measured operating baseline.
Evaluate how private and cloud resources fit together, including routing, observability, and data movement.
Your pilot defines the integration interface, monitoring, operating responsibilities, and commercial scope for the selected environment.
See how it works →Cost savings calculator
$1.8M saved every month
90% assumed savings rateIllustrative arithmetic based on your inputs, separate from the workload-specific infrastructure models above. Excludes fees and migration costs.
Monthly savings = monthly spend × savings rate. Annual savings assumes the same monthly spend for 12 months. Actual savings depend on your workload and deployment; a benchmark pilot validates what is achievable.
Technical exploration and enterprise conversations around more efficient AI inference.
Engaging with the engineering team of a leading semiconductor company to explore potential technical collaboration on AI inference optimization.
Discussed enterprise adoption of open models with a technical contact at a global automotive manufacturer. Relevant challenges include self-hosted inference costs, GPU efficiency, and deployment complexity.
How it works
Configure your workload, then start optimization and deployment with one click. Our agents tune the inference stack and deliver an endpoint with measured performance, quality checks, and estimated costs.
Our agents benchmark your workload, evaluate serving configurations, and tune runtime, precision, batching, caching, and parallelism against your objectives and constraints.
