SpecBundle
About SpecBundle
Speculative decoding, especially EAGLE3, offer strong theoretical guarantees alongside consistent empirical improvements in token acceptance rate and end-to-end inference speed. However, despite these advances, adoption of speculative decoding—especially EAGLE3—remains limited in the open-source ecosystem, due primarily to three key factors.
- Lack of production-ready training infrastructure: Existing speculative decoding toolchains are largely research prototypes, offering limited system-level optimization and inadequate support for diverse architectures and large-scale models.
- Scarcity of high-quality draft models: Effective speculative decoding depends on strong draft models, yet publicly available EAGLE3-compatible checkpoints are extremely limited, primarily originating from the original authors.
- Insufficient training scale of existing drafts: Most available draft models are trained on small or curated datasets and fail to generalize to the large, diverse corpora used in modern LLM training, resulting in low token acceptance rates and diminished practical speedups.
SpecBundle is a direct response to these limitations. Jointly driven by the open-source community and industry partners including Ant Group, Meituan, Nex-AGI and EigenAI, SpecBundle represents the first open initiative aimed at democratizing speculative decoding by providing high-performance, production-grade EAGLE3 draft model weights for mainstream open-source LLMs. This initiative also serves to verify the robustness of the SpecForge framework through multiple scales and architectures.
Installation
git clone https://github.com/sgl-project/SpecForge.git
Usage
Launch SGLang Server with SpecBundle models
You can use the following command to launch the SGLang server with SpecBundle models. Please add --tp, --ep and --mem-fraction-static arguments when you encounter memory issues.
python3 -m sglang.launch_server \
--model <target-model-path> \
--speculative-algorithm EAGLE3 \
--speculative-draft-model-path <draft-model-path> \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4
For example:
SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python3 -m sglang.launch_server \
--model Qwen/Qwen3-30B-A3B-Instruct-2507 \
--speculative-algorithm EAGLE3 \
--speculative-draft-model-path lmsys/SGLang-EAGLE3-Qwen3-30B-A3B-Instruct-2507-SpecForge-Nex \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4 \
--tp 4
Use SpecBundle to compare the performance of Speculative Decoding draft models
We provide a benchmark suite to evaluate the performance of SpecBundle draft models here.
Example:
- Launch a SGLang Server
SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python3 -m sglang.launch_server \
--model Qwen/Qwen3-30B-A3B-Instruct-2507 \
--speculative-algorithm EAGLE3 \
--speculative-draft-model-path lmsys/SGLang-EAGLE3-Qwen3-30B-A3B-Instruct-2507-SpecForge-Nex \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4 \
--tp 4
- Use the benchmark suite to evaluate the performance of SpecBundle draft models
bench_eagle3.py can help you launch a SGLang server process and a Benchmarking process concurrently. In this way, you don't have to launch the SGLang server manually, this script will manually handle the SGLang launch under different speculative decoding configurations. Some important arguments are:
--model-path: the path to the target model.--speculative-draft-model-path: the path to the draft model.--port: the port to launch the SGLang server.--trust-remote-code: trust the remote code.--mem-fraction-static: the memory fraction for the static memory.--tp-size: the tensor parallelism size.--attention-backend: the attention backend.--config-list: the list of speculative decoding configuration to test, the format is<batch-size>,<num-steps>,<topk>,<num-draft-tokens>.--benchmark-list: the list of benchmarks to test, the format is<benchmark-name>:<num-prompts>:<subset>.
cd SpecForge/benchmarks
python bench_eagle3.py \
--model-path Qwen/Qwen3-30B-A3B-Instruct-2507 \
--port 30000 \
--config-list 1,3,1,4 \
--benchmark-list mtbench:5 gsm8k:100 \
--skip-launch-server
Interactive Command Generator: Use the configuration selector below to automatically generate the appropriate test command for your model and benchmark.
python bench_eagle3.py \ --model-path meta-llama/Llama-3.1-8B-Instruct \ --port 30000 \ --config-list 1,3,1,4 \ --benchmark-list mtbench:5 ceval:5:accountant \ --tp-size 1 \ --mem-fraction-static 0.9 \ --trust-remote-code
It will generate a json file, content is listed below:
{
"mtbench": [
{
"batch_size": 1,
"steps": null,
"topk": null,
"num_draft_tokens": null,
"metrics": [
{
"latency": 12.232808108034078,
"output_throughput": 319.71399906382845,
"accept_length": 2.170366259711432,
"accuracy": null,
"num_questions": 5,
"num_valid_predictions": 0,
"categorical_performance": null
}
],
"num_samples": 5
}
],
"gsm8k": [
{
"batch_size": 1,
"steps": null,
"topk": null,
"num_draft_tokens": null,
"metrics": [
{
"latency": 37.42077191895805,
"output_throughput": 373.6160234823207,
"accept_length": 2.643410852713178,
"accuracy": 0.96,
"num_questions": 100,
"num_valid_predictions": 100,
"categorical_performance": null
}
],
"num_samples": 100
}
]
}
Performance Scores
We evaluate the performance of SpecBundle draft models on various benchmarks, please visit the Performance Dashboard for more details.