Eagle AI Corp
Eagle AI Corp develops and supports EAGLE speculative-decoding technology to make large-language-model inference substantially faster without changing model outputs. It describes its mission as achieving ultimate optimization through AI research, engineering, integration support, custom workflows and inference-speed partnerships.
At a Glance
- AI infrastructure companies
- LLM and generative-AI application developers
- Enterprise AI teams
- Model-serving and inference-platform providers
- +2 more
AI Tools by Eagle AI Corp
(1)
EAGLE-4
LLM Inference Speculative Decoding
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Latest News
EAGLE-4 v0.1.5 released with sampling-aware draft trees and SGLang startup/kernel improvements
EAGLE-4 v0.1.0 released as the initial eagle-harness implementation
EAGLE-4 launched on GMI Cloud, with the launch partner reporting 2.7x faster inference on NVIDIA B300 GPUs
AWS NxD Inference added EAGLE support
Products & Services
The first EAGLE speculative-decoding method, using extrapolated intermediate features to accelerate autoregressive LLM decoding while preserving the target model's output distribution.
A dynamic draft-tree evolution of EAGLE designed to improve inference speed through architectural advances in speculative decoding.
The faster, broader-coverage EAGLE generation; the company positions it as supporting nearly all major AI models and reports up to 5.6x over vanilla decoding on its site.
A flexible speculative decoder for mixture-of-experts and other LLM workloads. Its draft models work with eagle-harness, which sizes draft trees against measured hardware, batch and workload costs; the public implementation supports SGLang, vLLM and TokenSpeed.
Market Position
Eagle positions EAGLE as a leading speculative-decoding layer for fast, lossless LLM inference, emphasizing speed, broad model coverage and no accuracy loss. Its alternatives include built-in multi-token prediction (MTP), DSpark and DFlash 2; the EAGLE-4/GMI comparison reported EAGLE-4 ahead at low concurrency on Qwen3.8-27B while noting that MTP can be better for some sampling-heavy, higher-concurrency workloads.
Leadership
Founders
Hongyang Zhang
Co-founder and leader of Eagle; tenure-track Professor of AI at the University of Waterloo and leader of Safe AI Lab. He holds a PhD in machine learning from Carnegie Mellon University and graduated from Peking University; his research record includes the EAGLE papers and work associated with the University of Waterloo, Vector Institute and EAGLE AI Corp.
Executive Team
Hongyang Zhang
Co-founder and research/technology leader
University of Waterloo tenure-track Professor of AI and Safe AI Lab leader; PhD in machine learning from Carnegie Mellon University; graduate of Peking University; author/co-author of the EAGLE research line.
Founding Story
Eagle grew out of the EAGLE research line in speculative decoding. The company says it was hatched and is led by Hongyang Zhang and a group of AI researchers and engineering purists seeking new performance levels in AI; its initial vision was to turn the research into practical, high-speed inference infrastructure for AI builders and organizations.
Business Model
Revenue Model
A tiered membership and developer/enterprise-services model: paid community/developer plans, enterprise contact-based pricing, integration support, bespoke AI workflow development and technical speed partnerships.
Pricing Tiers
Starter plan for project supporters and EAGLE community members; includes a Discord badge and whitelist access to news, drops and products.
Developer plan for running AI-model inference with EAGLE; includes early access to releases and communication with the Eagle team.
Enterprise plan for custom AI workflows; includes custom integration support, bespoke workflow design, collaboration on future versions and unreleased breakthroughs.
Target Markets
- AI infrastructure companies
- LLM and generative-AI application developers
- Enterprise AI teams
- Model-serving and inference-platform providers
- Research organizations and AI labs
- GPU and systems ecosystem partners
- High-throughput serving of large language models
- Latency-sensitive coding assistants and interactive AI agents
- Enterprise custom AI workflows
- Production inference for model providers and AI applications
- Multi-GPU and high-concurrency LLM serving
- Inference optimization on NVIDIA and AMD accelerator hardware
- NVIDIA
- Meta
- Perplexity
- DeepSeek