EAGLE-4
Source-available speculative decoding drafter and eagle-harness package that accelerates LLM inference on SGLang, vLLM and TokenSpeed.
At a Glance
Royalty-free use under the Eagle 4 Source-Available License, subject to its restrictions.
Engagement
Available On
Alternatives
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.
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Pricing
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.
- 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
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
