Maximize AI inference
performance.

Prima is the optimization layer for the GPUs you already operate.

By continuously tuning across the inference stack — engine, sampling, model, hardware — we navigate the design space your team can't reach by hand.

No compression, no quality tradeoffs, no new hardware.

Built by the team that trained the largest AI models on the frontier — the world's first exascale supercomputer.

R&D 1002025 Winner
ACM Gordon Bell Finalist
$50M+DOE Funding
87%Sustained Efficiency
Meeting the requirements of the Agentic Era

Agentic workloads are bursty, long-running, and mixed

PRE-AGENTIC
Chat. RAG. Summarization.
~2023 — 2024
AGENTIC
Coding agents. Computer-use. Deep research.
2025 →
5–30×
More Tokens per Task
Versus chatbot turn. Coding agents push 100–1,000× when retries and context reloads are included.
100–300MW
Per Hyperscale Facility
Equivalent to the demand of a mid-sized city.

We need performance solutions that
satisfy token demand.

CLOSING THE GAP

More throughput without upending your stack.

Same model, same silicon, head-to-head against vLLM Default and the leading commercial inference providers.

Throughput vs vLLM Default · gpt-oss-120b
4× H100 DedicatedConcurrency = 256Decode-heavy
Throughput comparison vs vLLM Default baseline on gpt-oss-120b, 4× H100 DedicatedHorizontal bar chart. PrimaLabs Dedicated: +60% faster than vLLM Default. Fireworks Dedicated: +26% faster. vLLM Default: baseline. Fireworks Serverless: 35% slower. Together Serverless: 81% slower.05k10k15k20k25kPrimaLabsDedicated · 4×H100+60% fasterFireworksDedicated · 4×H100+26% fastervLLM DefaultDedicated · 4×H100DEFAULTFireworksServerless35% slowerTogetherServerless81% slowerOUTPUT TOKENS / SECOND
Source: PrimaLabs Performance Brief · May 2026 · gpt-oss-120b, 4× H100, decode-heavy, concurrency = 256Full methodology & cross-model results on request →
Throughput
+60%

vs vLLM Default, same silicon

The controller holds the fleet at its true ceiling in real time — not the conservative cap that leaves real tokens on the table.

Throughput improvement
+27%

vs the leading commercial dedicated provider

The controller reads hardware telemetry and acts through hardware and software — SLA-grade latency, no thermal throttling under load.

Throughput gain
27–97%

Across five frontier models

Hardware-, model-, and workload-aware maximization with no hardware changes and no quality regression.

HOW IT WORKS

A self-improving control system.

  • One, always-on loop; learns from every cycle.
  • Acts across the full host stack — engine, sampling, model placement, CPU and GPU.
  • Stays inside SLA and rack envelope at every step.
  • Invisible to workloads.
  • No operator intervention required.

Telemetry across CPU and GPU.

Engine config, sampling parameters, model state, CPU scheduling, GPU utilization, memory tiers — sampled in real time and modeled as a live signature of the fleet.

Autonomous control across the host stack.

The controller acts across the full stack, closing the loop between what the workload demands and what the hardware can deliver.

Gains that compound across the fleet.

The system learns the signature of every workload, every model, every deployment and carries that knowledge forward.

Results across the organization

For platform teams

Higher utilization and fewer emergency tuning cycles as workloads shift.

For finance teams

More revenue tokens from the same GPU and power envelope.

For customers

Better throughput under load without sacrificing latency targets.

For operators

Continuous control with explicit guardrails instead of one-time configs.

DEPLOYMENT

Deploy Prima optimization layer
in hours, without service interruption.

Prima sits above the serving stack

  • Integrates withvLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, Triton, Kubernetes, GPU telemetry
  • OptimizesBatching, scheduling, placement, power, runtime knobs
  • Governed bySLA, power, thermal, and quality guardrails
  • Delivered asOffline benchmark → shadow-mode validation → production control
Built for customers

Seeking inference performance edge.

Neoclouds

The performance edge customers feel.

For GPU-as-a-Service providers, PrimaLabs delivers measurably higher throughput and lower latency on the same fleet — a differentiator your customers benchmark and your sales team can sell.

Enterprise AI

An always-on performance layer.

For organizations running 100+ GPUs across multiple models, PrimaLabs continuously extracts the throughput your team would otherwise chase manually for quarters.

VPCOn-PremHybridZero Workload Migration
THE TEAM

The discipline of exascale.
Applied to agentic inference.

For two decades, the DOE national labs perfected one discipline: extracting maximum throughput from fixed hardware under hard power and thermal constraints. That is precisely the problem inference economics now faces. PrimaLabs is that discipline, applied — by the team behind ORBIT and the largest AI models ever trained on Frontier, the world's first exascale supercomputer.

Co-founder & CEO · Technical Vision
Director of AI Programs, Oak Ridge National Laboratory
  • Led ORBIT: 113B-parameter foundation model · 1.6 exaFLOPS sustained on 49,152 GPUs
  • ACM Gordon Bell Prize Finalist 2024 (ORBIT) & 2025 (ORBIT-2 · 4.1 exaFLOPS, 98% scaling)
  • White House panels · Tennessee AI Advisory Council
Prasanna Balaprakash
Co-Founder, President & COO
Four-time founder · zero-to-one across enterprise SaaS, AI, and healthtech
  • Built and scaled a venture studio incubating early-stage startups
  • Entrepreneur Magazine — Top 25 People in Tech
  • Operator background across GTM, fundraising, and product — pre-seed to growth
Chaitanya Hiremath
98%
Strong scaling efficiency · ORBIT-2 on 65,536 Frontier GPUs
ACM Gordon Bell Finalist · 2024 (ORBIT) & 2025 (ORBIT-2)
R&D 100
2025 Winner · the search engine inside PrimaLabs
Backed By
GET STARTED

Know your true ceiling.
Get started with a benchmark pilot.

Every GPU fleet is leaving tokens on the table. PrimaLabs benchmarks the workload, quantifies the upside, validates guardrails in shadow mode, and turns the winning configuration into a production control plane.

Buyer-safe by design: benchmark first, prove methodology, validate in shadow mode, then enable production controls inside explicit SLA, quality, power, and thermal guardrails.