Reserve capacity for real-time AI

Run high-throughput Blackwell GPU inference closer to users

Performance built for production AI demand

Real-time AI performance depends on throughput, latency, and where your inference runs. Powered by 96 GB of GDDR7 ECC memory and 1597 GB/s of memory bandwidth, the RTX PRO 6000 Blackwell on Akamai Cloud delivers the speed required for complex reasoning models.

Achieve up to 1.63× higher inference throughput than H100
Support up to 24,240 tokens per second per server
Improve throughput by 1.32× moving from FP8 to FP4
Support production-like demand with 100 concurrent inference requests
Run agentic AI, multimodal apps, vision AI, and media workloads

Reserve capacity

Connect with Akamai to discuss GPU capacity, performance goals, workload fit, and regional requirements.

How it works

Define your performance needs

Share your workload, concurrency goals, latency targets, and preferred regions.

Validate capacity requirements

Akamai will help review GPU capacity, benchmark fit, and deployment requirements.

Plan production rollout

Get guidance on reserving infrastructure for high-throughput AI inference workloads.

New study graphic stating Peak load latency cited as top scaling challenge for inference.

Performance problems show up when production traffic hits

A new study confirms what most AI teams already feel — peak load latency is the top scaling challenge in production inference. Akamai Cloud keeps you ahead of it, running inference closer to your users for faster, more consistent experiences when it matters most.

Performance proof points

Customer need	Akamai Cloud benchmark	Business outcome
Scale inference under load	Up to 1.63× higher throughput than H100 at 100 concurrent requests	Serve more AI requests without defaulting to larger, costlier GPU footprints
Generate responses faster	Up to 24,240 TPS per server	Improve response speed for high-volume AI
Keep latency low under concurrency	94.45 ms TTFT at 100 concurrent requests	Deliver faster first-token response for real-time AI experiences
Improve inference efficiency	1.32× throughput gain moving from FP8 to FP4	Get more output from the same GPU architecture
Support production demand	Benchmark used 100 concurrent runs with 200 input and 200 output tokens	Validate performance against realistic short prompt-response workloads

Built for AI workloads where performance is the product

Akamai Cloud helps teams run performance-sensitive AI workloads closer to users, devices, and data sources.

Conversational AI

Support assistants, copilots, voice agents, RAG workflows, and reasoning systems.

Multimodal AI

Run workloads that combine language, vision, speech, and decision-making.

Vision & Physical AI

Analyze camera, image, sensor, and video data closer to where it is created.

Media Processing

Accelerate video, rendering, graphics, image transformation, and visual workloads.