Eigen AI (Now part of Nebius)

Enterprise AI has moved beyond simple chatbots. To stay competitive, your product roadmap likely spans vision, video, speech, and text. Your infrastructure needs to be as ambitious as your vision. EigenInference is optimized for multimodal workloads, so as your product evolves from text to video or speech, performance stays consistent and seamless. Build for the future of AI, not just today’s requirements.

Speed is only valuable when it comes with accuracy. For enterprise-scale AI, “close enough” simply isn’t good enough. EigenInference uses advanced NVFP4 / FP3 quantization to deliver significant speedups while maintaining the model integrity your customers expect. Scale your AI services with the confidence that your outputs remain enterprise-grade. Fast, without the "correctness tax."

In the enterprise AI race, the biggest risk isn't the model—it's the friction between a great idea and a shipped product. High throughput isn't just a technical metric; it’s a business lever. By delivering strong tokens/sec across 10+ mainstream models (validated by Artificial Analysis), EigenInference enables faster feedback loops. Faster iteration. Smoother scaling. When your infrastructure keeps pace with your product roadmap, you do not just iterate—you outpace the market.

Eigen AI ranks #1 on Artificial Analysis for multiple popular models. 🚀 Today we’re excited to share that Eigen AI’s serving APIs are now listed on Artificial Analysis—with standout results in output speed, TTFT, and speed-for-price. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 • #1 output speed (tokens/s) among all GPU-based providers for GPT-OSS-120B, DeepSeek-V3.1-Terminus, and Qwen3-VL-235B-A22B 🚀 • Compelling speed-for-price: high throughput without premium pricing • Low latency at high speed (and low price): efficient placement on latency-vs-speed plots • Low TTFT: snappy first-token response for chat, agents, and copilots How we do it: 𝐄𝐢𝐠𝐞𝐧𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 A full-stack acceleration toolkit: FP4/NV-FP4 quantization (plus PTQ/QAT), sparsity, speculative decoding, KV-cache optimizations, and kernel/graph work (fused ops, custom CUDA/Triton). Why it stays up: 𝐄𝐢𝐠𝐞𝐧𝐃𝐞𝐩𝐥𝐨𝐲 Run serverless, on-demand, or dedicated. Traffic-aware autoscaling, hot restarts, and multi-replica HA keep latency and reliability steady. • Run anywhere: our managed cloud, your VPC/on-prem, or self-built clusters across regions • Observability & controls: token-level traces, metrics, budget alerts, audit logs • Uptime: 99.9%+ envelope Coming soon: 𝐄𝐢𝐠𝐞𝐧𝐂𝐥𝐨𝐮𝐝 Launching in a month—turn your SLA into a production API in a few clicks, powered by the same acceleration + reliability stack. Learn more & Compare results • Artificial Analysis provider view: https://lnkd.in/edYgbibm • Full blog: https://lnkd.in/eff-8RUW • Playground and API: https://app.eigenai.com/ • Eigen AI: https://www.eigenai.com/ Special thanks to Artificial Analysis team for the great efforts onboarding Eigen! #AI #LLM #Inference #MLOps #GenAI #Quantization #FP4 #Sparsity #SpeculativeDecoding #DevOps #Cloud #VPC #Agents #AEI

In the enterprise AI race, the biggest risk isn't the model, it's the friction between a great idea and a shipped product. High throughput isn't just a technical metric; it’s a business lever. By delivering strong tokens/sec across 10+ mainstream models (validated by Artificial Analysis), EigenInference enables faster feedback loops. When your infrastructure keeps pace with your product roadmap, you don't just iterate—you can outpace the market.

Before switching inference infrastructure, teams ask one thing: How does this compare to what we already use? On Artificial Analysis public benchmarks, Eigen AI ranks among the top performers across 25 mainstream models, providing a data-backed reference point against existing setups. Comparison should be based on data, not promises.