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    3. EAGLE-4
    EAGLE-4 icon

    EAGLE-4

    AI Infrastructure
    Featured

    Source-available speculative decoding drafter and eagle-harness package that accelerates LLM inference on SGLang, vLLM and TokenSpeed.

    Visit Website

    At a Glance

    Pricing
    Free tier available

    Royalty-free use under the Eagle 4 Source-Available License, subject to its restrictions.

    Commercial License: Custom/contact

    Engagement

    Available On

    Linux
    API
    SDK
    CLI

    Resources

    WebsiteDocsGitHubllms.txt

    Topics

    AI InfrastructureCompute OptimizationLocal Inference

    Alternatives

    TensorRT LLMLuceboxvLLM Ascend
    Developer
    Eagle AI CorpNew York City, NY

    Listed Nov 2026

    About EAGLE-4

    EAGLE-4 is the latest generation of the EAGLE family from Eagle AI Corp for accelerating large language model inference through speculative decoding. The repository ships eagle-harness, a pip-installable package that serves EAGLE-4 drafts on SGLang, vLLM and the experimental TokenSpeed engine. It is source-available software under the Eagle 4 Source-Available License, not an OSI-approved open-source license.

    What It Is

    EAGLE-4 (Extrapolation Algorithm for Greater Language-model Efficiency) is a drafter that adapts its speculative decoding strategy to batch size, hardware and workload while keeping the target model's output distribution. The draft uses a sliding-window draft layer, a low-rank candidate head and an append-only draft cache. Dense and latent-MoE draft structures are implemented, and Qwen3.8-27B with its EAGLE-4 draft is the measured target pair.

    How It Runs

    The eagle-harness package registers through hooks the inference engine exposes and does not patch the engine at runtime. Patched engines are distributed as pinned wheels and container images. An eagle-harness-check command verifies that the installed engine provides the hooks the plugin needs. On SGLang, the tree is sized from costs measured during served rounds, while vLLM uses a static tree whose depth stops on calibrated costs produced with a calibration command.

    Supported Hardware and Limits

    Documented engines are SGLang on NVIDIA B300 (CUDA 13.0) and AMD MI355X (ROCm 7.2), vLLM on NVIDIA B300, and experimental CUDA-only TokenSpeed. Kernels target NVIDIA sm_80 and newer and AMD CDNA2 to CDNA4 through Triton. Requests are text only, and pipeline parallelism is not supported. The README reports measured speedups over no-speculation controls, for example 1.91x on SGLang with 4x B300 at concurrency 8 and 2.99x on vLLM at concurrency 1.

    Licensing Model

    The license permits research, evaluation, education and noncommercial use. Commercial use is allowed within conditions including a US $20 million aggregate revenue threshold, and competing offerings need a separate written commercial license. Commercial inquiries go to licensing@eagle.ai.

    EAGLE-4 - 1

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    Pricing

    FREE

    Source Available

    Royalty-free use under the Eagle 4 Source-Available License, subject to its restrictions.

    • Research, evaluation, education, and noncommercial use
    • Noncompeting commercial use permitted subject to a $20 million aggregate revenue threshold across the licensee and affiliates
    • Attribution and license notices required
    • Not an OSI-approved open-source license

    Commercial License

    Contact licensing@eagle.ai for separately negotiated rights. Pricing is not published.

    Custom
    contact sales
    • Required for commercial use after exceeding the revenue threshold
    • Required for competing offerings regardless of revenue
    • Availability and terms subject to agreement with Eagle AI Corp
    View official pricing

    Capabilities

    Key Features

    • Speculative decoding drafter that adapts to batch size, hardware and workload
    • Preserves the target model's output distribution
    • Sliding-window draft layer, low-rank candidate head and append-only draft cache
    • Dense and latent-MoE draft structures
    • Engine plugins for SGLang, vLLM and experimental TokenSpeed
    • Cost-based speculative tree sizing with calibration command
    • Triton kernels for NVIDIA and AMD GPUs
    • Pinned wheels and container images for engines
    • eagle-harness-check hook verification command

    Integrations

    SGLang
    vLLM
    TokenSpeed
    Hugging Face
    NVIDIA B300
    AMD MI355X
    FlashInfer
    API Available
    View Docs

    Ratings & Reviews

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    Developer

    Eagle AI Corp

    Eagle Corp is led by a group of AI researchers and engineering purists working on AI performance. The about page says it is led by Dr. Hongyang Zhang, a tenure-track Professor of AI at the University of Waterloo and leader of Safe AI Lab. The company develops the EAGLE family of speculative decoding methods.

    New York City, NY
    30 employees

    Used by

    NVIDIA (Blackwell and Jetson Thor…
    Meta (Llama 4 Maverick)
    Perplexity (Sonar)
    DeepSeek (DeepSeek-V3 technical report)
    +4 more
    Read more about Eagle AI Corp
    WebsiteGitHubX / Twitter
    1 tool in directory

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