Atlas Inference: Doing DeepSeek Better Than DeepSeek

Atlas Inference is Atlas Cloud’s production infrastructure platform for large language models, designed to address latency, GPU costs, and operational complexity that often arise when AI systems move from proof-of-concept to deployment. Running on NVIDIA H100 clusters, it claims to outperform DeepSeek’s reference deployments of DeepSeek R1 and V3 through techniques including prefill-decode disaggregation, overlapping compute and communication batches, and automated load balancing for mixture-of-experts models. Atlas reports per-node prefill throughput of 51.7 thousand tokens per second, decode throughput of 22.5 thousand tokens per second, a 4.47-second median time to first token, inter-token latency of 100 milliseconds or less, and an 81% profit margin. The company positions these capabilities as a way for organizations to operate larger-context and agentic AI workloads with fewer GPUs, lower total cost of ownership, more predictable performance, and less infrastructure-management effort, and says the service is currently available to select design partners.

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