EAGLE-LLaMA3-Instruct-8B
EAGLE is a speculative decoding framework that accelerates LLM inference by 2–5.6× without degrading output quality. It works by predicting future token probabilities using intermediate model layers, then verifying predictions against the actual model. The approach is trainable in 1–2 days on modest hardware (8× RTX 3090) and integrates with major serving frameworks (vLLM, TensorRT-LLM, SGLang, etc.). This model checkpoint is EAGLE-3 optimized for LLaMA-3-Instruct-8B.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | yuhuili |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 110.5k |
| Likes | 5 |
| Last updated | 2025-09-19 |
| Source | yuhuili/EAGLE-LLaMA3-Instruct-8B |
What EAGLE-LLaMA3-Instruct-8B is
EAGLE-3 removes feature prediction constraints and fuses low-, mid-, and high-level semantic features for draft tree generation. It uses training-time testing to simulate the speculative decoding process. The published benchmarks show 5.6× speedup over vanilla autoregressive decoding on 13B models (Vicuna), though this specific 8B checkpoint's measured performance is not stated. Implementation relies on feature extrapolation from second-to-top layers and dynamic tree acceptance via confidence scoring. Compatible with quantization, FlashAttention, vLLM, and other optimization techniques.
Run EAGLE-LLaMA3-Instruct-8B locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="yuhuili/EAGLE-LLaMA3-Instruct-8B")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Estimated 16–24 GB VRAM per GPU for fp16 inference of base + draft model combined (EAGLE-3 runs two forward passes). Benchmarks use 2× RTX 3090 (24 GB each) for Vicuna 13B; 8B variant likely feasible on single 24 GB GPU but concurrent request batching may require multi-GPU. Quantization (int8, int4) can reduce footprint. CPU memory ≥32 GB recommended for model loading and metadata.
Unknown. Model card does not document LoRA, QLoRA, or further fine-tuning procedures for this checkpoint. The framework mentions training drafts in 1–2 days on 8× RTX 3090, suggesting custom fine-tuning is possible but requires access to EAGLE's training code (external to this HF checkpoint). Requires review of SafeAILab/EAGLE GitHub repository for training scripts.
When to avoid it — and what to weigh
- Strict determinism required — Speculative decoding introduces sampling variance during draft acceptance. If exact reproducibility is non-negotiable, verify acceptance behavior matches your requirements.
- Single-GPU, CPU-only, or extremely resource-constrained environments — EAGLE requires running both draft and base models in memory. The 8B base model alone will not fit on most consumer GPUs under 24 GB VRAM without quantization.
- Unproven or custom LLaMA-3 variants — This checkpoint is trained on LLaMA-3-Instruct. Using it with significantly different architectures or fine-tuned versions may degrade or invalidate speedup claims.
- Mission-critical systems without in-house validation — Benchmarks reference Vicuna 13B; real-world performance on this 8B checkpoint and your specific hardware/quantization is unknown and must be measured.
License & commercial use
Apache License 2.0 (OSI-approved permissive license). Permits commercial use, modification, and distribution, with requirements for license preservation and liability disclaimer.
Apache-2.0 explicitly permits commercial use of the model and code. No gating, no custom restrictions. However, ensure your integration (vLLM, TensorRT-LLM, etc.) also complies with their licenses. Base model (LLaMA-3) is subject to Meta's LLaMA 3 Community License; confirm that separately if using official Meta weights. This checkpoint does not remove that underlying constraint.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security statement provided in model card. Considerations: (1) Speculative decoding runs two inference passes; ensure both base and draft models are sourced from trusted origins; (2) Model card does not disclose training data composition or potential biases; (3) LLaMA-3 base model safety training unknown; (4) Inference framework (vLLM, TensorRT-LLM) security is separate concern; (5) No mention of adversarial robustness or jailbreak mitigations. Recommend threat modeling for your deployment context.
Alternatives to consider
Medusa (Fast Inference with Medusa Heads on Language Models)
Earlier speculative decoding approach. EAGLE-3 claims 1.8× speedup over EAGLE-1 and comparisons show EAGLE-1 ~1.6× faster than Medusa. Medusa may have wider ecosystem support and simpler training, but lower absolute throughput.
Lookahead Decoding (Google/OpenAI research)
Non-parametric speculative method. EAGLE-2 claims 2× speedup over Lookahead (13B). Lookahead avoids training a draft model but may not scale as aggressively; explore if training overhead is prohibitive.
vLLM Paged Attention + LoRA (serving without speculative decoding)
If hardware permits, vanilla vLLM with batching and kernel optimization may be simpler to deploy than adding speculative decoding. Measure throughput before committing to EAGLE training/integration effort.
Ship EAGLE-LLaMA3-Instruct-8B with senior software developers
Download EAGLE-LLaMA3-Instruct-8B from Hugging Face, validate speedup on your hardware, and integrate with vLLM or TensorRT-LLM. Benchmark real latency before production deployment.
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EAGLE-LLaMA3-Instruct-8B FAQ
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Custom software development services
Need help beyond evaluating EAGLE-LLaMA3-Instruct-8B? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to Accelerate Your LLM Inference?
Download EAGLE-LLaMA3-Instruct-8B from Hugging Face, validate speedup on your hardware, and integrate with vLLM or TensorRT-LLM. Benchmark real latency before production deployment.