Eigen AI now on Artificial Analysis

Today we’re excited to announce that Eigen AI’s serving APIs are now officially listed on Artificial Analysis—with standout results across output speed, TTFT, and speed-for-price.

Highlights:

Top output speed (tokens/sec)
In Artificial Analysis, Eigen AI ranks #1 in output throughput on DeepSeek-V3.1-Terminus, Qwen3-VL-235B-A22B, and GPT-OSS-120B benchmarks, achieving 791 tokens per second—the fastest among all evaluated GPU-based providers. This record speed ensures users experience rapid, seamless token streaming from the moment generation begins, delivering both speed and consistency across workloads.

GPT-OSS-120B throughput ranking on Artificial Analysis.

DeepSeek V3.1 benchmark results for GPU-based providers.

Qwen3 VL 235B A22B performance for BF16 GPU-based providers
Compelling speed-for-cost ratio.
Eigen AI endpoints sit on the favorable frontier of tokens/sec vs. $/token, delivering high throughput without premium pricing.

In the latest benchmark results, Eigen AI leads the efficiency quadrant — combining top-tier output speed with outstanding cost performance.
Low latency at high speed (and low price).
On Artificial Analysis, Eigen AI stands out on the latency-vs-speed benchmark, combining fast first-token response with sustained high-speed output.

The results show that Eigen AI can process more requests per GPU while maintaining quick starts and steady streams — offering real-time responsiveness at a competitive overall cost.
Low TTFT (Time to First Token).
Our models maintain consistently low Time to First Token (TTFT) on Artificial Analysis, meaning responses begin almost instantly after receiving a prompt.

This quick response improves the real-time experience for chatbots, agents, and copilots, allowing them to start conversations faster and handle more interactions within the same time frame.

Try out our playground and API at: Eigen AI Model Studio.

Why inference speed (and stability) determines product quality

Latency determines UX; throughput determines cost. The core challenge is maintaining consistent sub-SLA latency and predictable unit economics at scale.

Eigen AI combines quantization-aware training (QAT), kernel-level optimization, and production-grade scheduling, sustaining low-variance inference under load so teams can ship faster, scale wider, and maintain reliability without cost volatility.

EigenInference: a full-stack acceleration toolkit

1) Quantization that actually holds quality

FP4 / NV-FP4 with per-channel/group scaling for text and diffusion.
PTQ for fast wins using calibration sets.
QAT to recover quality on sensitive tasks.

In practice:

For Flux and Qwen-Image-Edit, FP4 yields up to 4× faster inference, fits on 24 GB GPUs, and renders high-quality edits in ~1.8 s—with no perceptible regression on visual fidelity.

2) Sparsity and structured efficiency

Static/dynamic sparsity (e.g., N:M), sparse/hybrid attention for long context.
Distillation to specialists to cut latency and cost without losing task quality.

3) Throughput primitives

Continuous/Adaptive batching, speculative decoding, and KV-cache ops (paged attention, smart eviction, cache quantization).

4) Kernel & graph optimizations

Fused MHA/MLP, Flash-style attention, custom CUDA/Triton kernels, graph capture, and GPU↔CPU hybrid offload.

5) Policy routing & modality coverage

Route by SLA/cost/guardrails across model families and shards.
Works across LLM, VLM, image, and video generation from one control plane.

Takeaway: It’s never one trick. We assemble the right mix—quantization, sparsity, batching, KV-cache logic, speculative decoding, and kernel tuning—to meet your latency, stability, and serving-efficiency targets.

EigenDeploy: reliable GPU serving and orchestration

Serving modes for different workloads and scaling needs

Serverless for instant endpoints that absorb spiky traffic.
On-demand for predictable usage with cost visibility.
Dedicated for strict isolation for steady, high-volume workloads.

Scaling, failover, and uptime stability

Traffic-aware autoscaling up/down by queue depth, tokens/sec, or GPU utilization.
Hot restarts on error bursts, with multi-replica HA to eliminate downtime.
Blue/green & canary deployments for instant rollback to protect customer traffic.

Run anywhere: cloud, on-prem, or hybrid GPU clusters

Deploy on our managed cloud, your VPC/on-prem, or self-built GPU clusters — all supported under a unified orchestration layer with multi-region footprints.

Observability and guardrails

Token-level traces, real-time metrics, and automated budget alerts.
Per-request audit logs, SSO, and fine-grained access for compliance teams.

SLA: 99.9%+ uptime envelope with built-in controls for regulated environments.

The Eigen GPU Cloud: managed acceleration platform (launching soon)

We’re rolling out Eigen’s GPU Cloud, a managed platform that turns your SLA into a production-grade API in just a few clicks.

It ships with the same acceleration stack (FP4 / PTQ / QAT/sparsity/kernels) and the same operational guarantees (autoscaling, hot restarts, HA, observability). Bring your model or choose from our catalog; ship without friction.

What you get with Eigen

Lower latency, lower cost via FP4/PTQ/QAT, sparsity, speculative decoding, and fused kernels.
Predictable operations with autoscaling, hot restarts, and multi-replica HA.
Flexible deployment options — serverless, on-demand, or dedicated — on our cloud or yours.
End-to-end visibility with real-time metrics and compliance-ready controls.
Fast path to production with the upcoming Eigen GPU Cloud.

Artificial Efficient Intelligence — AGI Tomorrow, AEI Today.

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