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Introducing Atlas Inference

Summary

Atlas Inference removes the biggest constraints limiting AI model performance.
Atlas Inference outperforms the DeepSeek team with its own private deployments of DeepSeek R1 and V3 with a fraction for the GPUs.
Atlas Inference provides a safe, simple, scalable, and cost-effective foundation for deploying the larger, more complex models of the future.

Why AI Feels Fast in the Lab but Slow (and Expensive) in Production

Understanding the problem

Large-language models (LLMs) now draft emails, write code, even design chips. Yet when enterprises move those models from PoC to production, three familiar constraints appear:

Unpredictable latency. Users wait while an LLM digests ever-longer prompts.
Run-away GPU bills. Cloud-on-demand pricing punishes sustained workloads.
Operational complexity. Engineering teams chase fractional gains in tokens-per-second across hundreds of accelerators.

Those bottlenecks block real value creation for boards and business leaders, and faster model training alone won’t solve them. Inference is now the principal cost and performance driver.

DeepSeek built its reputation by profitably solving for these inference hurdles. They created some of the most cost-effective and high-performing AI models to date, DeepSeek V3 and DeepSeek R1.

Introducing Atlas Inference

Purpose-built infrastructure for production LLMs

Atlas Inference is Atlas Cloud’s answer to the inference wall. Running on NVIDIA H100 clusters, it re-architects how prompts are processed and how GPU resources are scheduled.

Crucially, Atlas Cloud now outperforms DeepSeek with its own private deployments of DeepSeek R1 and V3, delivering:

KPI (per node)	Result	Why it matters
Prefill throughput	51.7 k tokens/sec	60 % higher than DeepSeek’s own reference, so models start reasoning sooner.
Decode throughput	22.5 k tokens/sec	52 % faster final-token generation; smoother conversations.
Median time-to-first-token	4.47sec	Keeps user attention on every channel.
Median inter-token latency	≤ 100msec	Chat feels fluid and human-like.
Profit margin	81%	Sustainable economics compared to hyperscaler GPU-on-demand.

Under the Hood

We engineered for outcome, then simplified the means

We separated the heavy lifting. Prefill-Decode (PD) Disaggregation runs your long prompt analysis on a few GPUs optimised for compute, while lightweight token generation flows on memory-rich GPUs. This removes the “traffic-jam” effect that slows every request as load rises. (See the latency chart on slide 4: decode latency stays flat even as batch sizes jump.)

We kept GPUs busy, not idle. Two-Batch Overlap pipelines communication and calculation so hardware is never waiting for its turn.

We balanced the experts. For mixture-of-experts models, our Expert Parallelism Load Balancer shifts work away from hotspots automatically, avoiding the 80 / 20 utilisation trap.

The net result: consistent sub-five-second first tokens and predictably low inter-token latency, without the fleet-size overprovisioning required from traditional clouds.

What This Means for the Industry

Calling all disruptors and innovators

For senior leaders, the real measure of AI success is not the size of the model but the business outcomes it unlocks in production. We believe Atlas Inference turns raw technical speed into board-level value by removing operational drag. Here’s what that means in concrete terms:

Higher-quality answers per dollar. More tokens processed per second lets models “think” longer, improving reasoning scores on benchmarks and, more importantly, on your proprietary tasks.
Lower total cost of ownership. Bare-metal efficiency plus PD Disaggregation means fewer nodes for the same workload, a direct line to healthier unit economics.
Future-proof capacity. Atlas Inference scales from a handful of GPUs to thousands, matching demand curves without the penalty pricing of hyperscalers.
Simpler operations. Your teams focus on product features, not GPU scheduling scripts. We deliver the “Safe, Simple, Scalable” infrastructure promise baked into every Atlas Cloud service.

Unlocking the Next Wave of AI Performance

Inferring the road ahead

As context windows grow and multi-modal models mature, competitive advantage will hinge on inference efficiency rather than model size.

Atlas Inference enables organisations to run larger-context versions of today’s models without latency spikes, orchestrate agentic workflows that chain multiple model calls together while still meeting real-time expectations, and offer premium, high-margin AI services to customers who previously found them cost-prohibitive.

Ready to Experience Production-Grade Speed?

Atlas Inference is available today for select design-partners. Follow us on LinkedIn for performance deep-dives, customer case studies, and early-access opportunities.

— The Atlas Cloud Team