# eagle-hq/EAGLE-4 > Official inference harness for EAGLE-4 speculative decoding, adding a flexible drafter plugin to SGLang, vLLM and TokenSpeed that adapts to batch size, hardware and workload. It speeds up serving of LLMs such as Qwen3.8-27B while preserving the target model's output distribution. - Magnitude: 5.1 out of 10 — Early signal - Stars: 137 total · Not enough measurements today - Star trust: star growth looks organic - Category: Language models · Language: Python · Created: 2026-10-20 · Last push: 2026-10-20 - GitHub: https://github.com/eagle-hq/EAGLE-4 · Homepage: https://eagle.ai · Page: https://gitnova.dev/en/r/eagle-hq/EAGLE-4 ## Useful for - Speed up Qwen3.8-27B serving by installing the eagle-harness plugin on SGLang or vLLM - Run the prebuilt Docker image with the patched engine and EAGLE-4 on NVIDIA B300 GPUs - Verify that the installed engine exposes all hooks the EAGLE-4 plugin needs using eagle-harness-check ## Why it’s here - There are not enough fresh counter measurements for today's UTC day. The gain is unknown. - The repository is 1 day old and already has 137 stars. With less than two weeks of history, there's no usual pace to compare the spike against yet. - Top new repositories this week: #134. ## Star trust Star growth looks organic. Star-trust labels are heuristics based on the repository’s behavior, not a check of every stargazer. ## Numbers - Forks: 2 - Issues and pull requests: 0 - Watchers: 0 - Average over the last week: 142 per day - Usual pace: too little history (under two weeks) - Latest release: v0.1.0 (2026-10-20) ## Stars per day, last 2 days (oldest → newest, today is partial) 2026-10-20 … 2026-10-21: 143, None ## Spotted in now - Top new repositories this week: #134 ## More in this category 1. **deepseek-ai/DeepInfer** — 10.0 · Breakout · Language models · Python · Not enough measurements today Offline inference framework for running DeepSeek models at large scale: optimized kernels, MoE/Attention parallelism, deterministic sampling and structured outputs. Aimed at teams deploying DeepSeek-V4.1-Flash on NVIDIA SM100 or Huawei… Full card: https://gitnova.dev/en/r/deepseek-ai/DeepInfer.md 2. **google-pytorch/torch_tpu** — 7.2 · Early signal · Language models · C++ · Not enough measurements today TorchTPU is a PyTorch backend for Google TPUs that lets you run PyTorch models and operations on TPU devices, with custom ATen kernels, an XLA compilation cache, and distributed training support. Full card: https://gitnova.dev/en/r/google-pytorch/torch_tpu.md 3. **vllm-project/vllm** — 5.3 · Breakout · Language models · Python · Not enough measurements today A high-throughput and memory-efficient inference and serving engine for large language models, using PagedAttention for KV-cache management. Supports 200+ model architectures and an OpenAI-compatible API. Full card: https://gitnova.dev/en/r/vllm-project/vllm.md 4. **MiaAI-Lab/DeepSeek-v4.1-Flash-1017-2x-DGX-Sparks** — 5.1 · Early signal · Language models · Shell · Not enough measurements today Scripts to serve the quantized DeepSeek-V4.1-Flash-1017 (EXL3, 2.9 bpw) across two NVIDIA DGX Sparks via the TensorFold engine, exposing an OpenAI-compatible API with up to a 1M-token window and DSpark speculative decoding. Full card: https://gitnova.dev/en/r/MiaAI-Lab/DeepSeek-v4.1-Flash-1017-2x-DGX-Sparks.md 5. **jatechan/ai-data-extractor** — 5.0 · Early signal · Language models · Python · Not enough measurements today Python tool that extracts local chat histories from AI coding assistants (Claude Code, Cursor, Windsurf, Aider, Cline/Roo Code and more) into a unified JSONL format for fine-tuning, analytics, or backup. Full card: https://gitnova.dev/en/r/jatechan/ai-data-extractor.md --- Magnitude (0–10) measures how fast and how unusually interest in a repository is growing right now. It is not a quality score. Days are UTC. “So far today” is a fact; “expected by the end of the day” is a forecast. Summaries and use cases are written by an LLM (DeepSeek V4.1 Flash) from the README and may be inaccurate: verify specific claims (benchmarks, speed, hardware) in the repository itself. Data as of 2026-10-21 00:15 UTC, updated every 30 minutes.