Dart Diffusion Inspired Speculative Decoding

DART (Diffusion-Inspired Speculative Decoding) enables Claude to help users set up, configure, and integrate a speculative decoding framework that achieves 2x-3.4x wall-clock speedup over standard autoregressive LLM inference. Unlike EAGLE3, which drafts tokens autoregressively (creating a bottleneck in the draft stage itself), DART predicts logits for multiple future masked positions in parallel through a single forward pass of a lightweight transformer layer, then constructs high-quality draft token trees using an N-gram-enforced pruning algorithm implemented in C++.

When to Use

• When the user wants to accelerate inference for Qwen3-series models (1.7B through 32B) using speculative decoding
• When the user asks to set up DART from the fvliang/DART GitHub repository, including installation, model download, and configuration
• When the user wants to compare DART against EAGLE2/EAGLE3 baselines on their workloads
• When the user needs to integrate DART's Python API into an existing serving pipeline or Gradio demo
• When the user is building a speculative decoding system and wants to understand the parallel drafting + tree pruning approach
• When the user asks about optimizing LLM inference latency and speculative decoding is a viable strategy
• When the user wants to train or fine-tune a DART draft model head for a new base model

Key Technique

The Drafting Bottleneck Problem. Standard speculative decoding uses a small "draft" model to propose candidate tokens, which the larger "target" model then verifies in parallel. Methods like EAGLE3 improve draft accuracy but still generate candidates autoregressively -- each draft token depends on the previous one, requiring multiple sequential forward passes through the draft model. This makes the drafting stage itself a latency bottleneck, especially as draft sequence length grows.

DART's Parallel Drafting. Inspired by diffusion-based language models (dLLMs), DART eliminates autoregressive rollouts in the draft model entirely. It takes hidden states from an intermediate layer of the target model and feeds them into a single lightweight transformer layer that predicts logits for multiple future token positions simultaneously in one forward pass. This is analogous to how diffusion models denoise multiple positions in parallel -- the draft model "fills in" masked future positions all at once rather than left-to-right. The result is dramatically lower drafting latency: one forward pass through one transformer layer versus N sequential passes through a multi-layer draft model.

N-gram-Enforced Tree Pruning. Raw parallel predictions lack the sequential coherence of autoregressive generation. DART compensates with a C++-implemented tree pruning algorithm that takes the top-K candidates at each position and filters them using N-gram frequency statistics from a prebuilt N-gram model. This enforces semantic continuity -- candidate branches that form implausible N-gram sequences are pruned, producing compact, high-quality draft trees that the target model can verify efficiently. The combination of cheap parallel drafting + smart tree construction yields 30% higher throughput than EAGLE3 on average, with up to 65% improvement on code-centric workloads.

Step-by-Step Workflow

1. Install DART and dependencies. Clone the repository and use uv for dependency management:

   git clone https://github.com/fvliang/DART.git
   cd DART
   curl -LsSf https://astral.sh/uv/install.sh | sh
   uv sync
   uv pip install -e .
   

2. Select the appropriate model trio. DART requires three components: a base model, a DART draft head, and an N-gram model. Match the DART head to the base model size:

   Base Model	DART Head	N-gram Model
   Qwen/Qwen3-1.7B	fvliang/qwen1.7b-dart	fvliang/dart-qwen3-ngram
   Qwen/Qwen3-4B	fvliang/qwen4b-dart	fvliang/dart-qwen3-ngram
   Qwen/Qwen3-8B	fvliang/qwen8b-dart	fvliang/dart-qwen3-ngram
   Qwen/Qwen3-14B	fvliang/qwen14b-dart	fvliang/dart-qwen3-ngram
   Qwen/Qwen3-32B	fvliang/qwen32b-dart	fvliang/dart-qwen3-ngram

3. Load the model in Python. Use DartModel.from_pretrained with all three paths:

   import torch
   from dart import DartModel
   
   model = DartModel.from_pretrained(
       base_model_name_or_path="Qwen/Qwen3-8B",
       dart_model_name_or_path="fvliang/qwen8b-dart",
       ngram_model_name_or_path="fvliang/dart-qwen3-ngram",
       torch_dtype=torch.float16,
       device_map="auto",
       is_small_ngram=False  # True for faster loading during testing
   )
   

4. Format the input using the chat template registry. DART uses TEMPLATE_REGISTRY for prompt formatting:

   from dart import TEMPLATE_REGISTRY
   template = TEMPLATE_REGISTRY["qwen"]
   prompt = template.format(messages=[{"role": "user", "content": "Explain speculative decoding."}])
   

5. Run inference with dart_generate. Configure sampling parameters and generation limits:

   from dart import dart_generate
   
   output = dart_generate(
       model,
       prompt=prompt,
       temperature=0.7,
       top_p=0.9,
       top_k=50,
       max_new_token_num=512,
       max_length=2048,
   )
   

6. Launch a Gradio demo for interactive testing. Use the provided shell scripts or the direct CLI:

   uv run python dart/app/app.py \
       --base-model-name-or-path Qwen/Qwen3-4B \
       --dart-model-name-or-path fvliang/qwen4b-dart \
       --ngram-model-name-or-path fvliang/dart-qwen3-ngram \
       --device cuda \
       --max-new-tokens 2048 \
       --server-port 30000
   

7. Benchmark against EAGLE3. Add the --compare-eagle3 flag to the Gradio app to run side-by-side comparisons on the same prompts.

8. Tune tree construction parameters. Adjust the top-K candidate count and N-gram pruning aggressiveness based on your latency/accuracy tradeoff. Larger K gives more candidates but bigger verification trees; stricter N-gram filtering gives smaller trees but may miss valid continuations.

9. Use --use-small-ngram for rapid prototyping. This flag loads a reduced N-gram model that is faster to initialize, useful during development before running full benchmarks.

10. Profile end-to-end latency. Measure three components separately: (a) target model forward pass, (b) DART draft head forward pass, and (c) tree construction + verification. DART's advantage is specifically in reducing component (b) to a single pass.

Concrete Examples

Example 1: Setting up DART for a Qwen3-8B deployment

User: "I want to speed up my Qwen3-8B inference using DART speculative decoding. Help me set it up."

Approach:

1. Clone the DART repo and install dependencies with uv sync && uv pip install -e .
2. Verify CUDA availability and GPU memory (8B model needs ~16GB in fp16, plus overhead for DART head)
3. Write a Python script that loads DartModel.from_pretrained with Qwen/Qwen3-8B, fvliang/qwen8b-dart, and fvliang/dart-qwen3-ngram
4. Format a test prompt using the Qwen chat template
5. Call dart_generate with standard sampling parameters
6. Print both the output text and the generation speed (tokens/sec)

Output:

import torch, time
from dart import DartModel, dart_generate, TEMPLATE_REGISTRY

model = DartModel.from_pretrained(
    base_model_name_or_path="Qwen/Qwen3-8B",
    dart_model_name_or_path="fvliang/qwen8b-dart",
    ngram_model_name_or_path="fvliang/dart-qwen3-ngram",
    torch_dtype=torch.float16,
    device_map="auto",
    is_small_ngram=False,
)

template = TEMPLATE_REGISTRY["qwen"]
prompt = template.format(messages=[{"role": "user", "content": "Write a Python quicksort."}])

start = time.time()
output = dart_generate(model, prompt=prompt, temperature=0.0, top_k=1, max_new_token_num=256, max_length=2048)
elapsed = time.time() - start
print(f"Generated {len(output.tokens)} tokens in {elapsed:.2f}s ({len(output.tokens)/elapsed:.1f} tok/s)")
print(output.text)

Example 2: Launching a Gradio demo comparing DART vs EAGLE3

User: "I want to visually compare DART and EAGLE3 speed on my prompts."

Approach:

1. Confirm the DART repo is installed and models are downloaded
2. Launch the Gradio app with the --compare-eagle3 flag
3. Provide the user with the URL to access the web interface

Output:

cd DART
uv run python dart/app/app.py \
    --base-model-name-or-path Qwen/Qwen3-4B \
    --dart-model-name-or-path fvliang/qwen4b-dart \
    --ngram-model-name-or-path fvliang/dart-qwen3-ngram \
    --device cuda \
    --max-new-tokens 2048 \
    --compare-eagle3 \
    --server-port 30000
# Open http://localhost:30000 to see side-by-side generation

Example 3: Integrating DART into an existing serving script

User: "I have a batch inference script that processes prompts from a JSONL file. How do I swap in DART?"

Approach:

1. Replace the standard model.generate() call with dart_generate()
2. Load the DART model trio instead of the plain base model
3. Keep the existing prompt formatting and output handling
4. Add is_small_ngram=True for initial testing, switch to False for production

Output:

import json, torch
from dart import DartModel, dart_generate, TEMPLATE_REGISTRY

model = DartModel.from_pretrained(
    base_model_name_or_path="Qwen/Qwen3-8B",
    dart_model_name_or_path="fvliang/qwen8b-dart",
    ngram_model_name_or_path="fvliang/dart-qwen3-ngram",
    torch_dtype=torch.float16,
    device_map="auto",
    is_small_ngram=False,
)
template = TEMPLATE_REGISTRY["qwen"]

with open("prompts.jsonl") as f:
    prompts = [json.loads(line) for line in f]

results = []
for item in prompts:
    formatted = template.format(messages=[{"role": "user", "content": item["prompt"]}])
    output = dart_generate(
        model, prompt=formatted,
        temperature=item.get("temperature", 0.7),
        top_p=0.9, top_k=50,
        max_new_token_num=item.get("max_tokens", 512),
        max_length=2048,
    )
    results.append({"prompt": item["prompt"], "response": output.text})

with open("results.jsonl", "w") as f:
    for r in results:
        f.write(json.dumps(r) + "\n")

Best Practices

• Do: Use torch.float16 (or bfloat16 on Ampere+ GPUs) for both the base model and DART head to minimize memory and maximize throughput.
• Do: Start with is_small_ngram=True during development to speed up model loading, then switch to the full N-gram model for production benchmarks.
• Do: Set temperature=0.0 and top_k=1 when benchmarking raw speedup, to isolate the speculative decoding performance from sampling variance.
• Do: Profile the three stages separately (target forward, draft forward, tree construction) to identify where your specific bottleneck lies.
• Avoid: Using DART with models other than the supported Qwen3 series without first training a compatible DART draft head -- the hidden state dimensions and layer semantics must match.
• Avoid: Setting excessively large max_new_token_num without also increasing max_length, as the tree verification requires buffer space beyond the raw token count.
• Avoid: Expecting identical output to vanilla autoregressive decoding when using non-zero temperature -- speculative decoding is lossless only with greedy sampling; stochastic sampling introduces minor distribution shifts depending on the verification scheme.

Error Handling

Limitations

• Model support is currently limited to the Qwen3 family. Using DART with LLaMA, Mistral, or other architectures requires training a new draft head on that model's hidden states.
• Single-request latency optimization only. DART does not address batched/continuous batching scenarios directly -- its speedup is per-request. Integration with vLLM or TGI would require custom verification kernels.
• The N-gram model adds memory overhead. The full N-gram model can consume several GB of RAM; on memory-constrained systems, is_small_ngram=True is necessary but reduces acceptance rates.
• Greedy-only losslessness. Like all speculative decoding methods, DART guarantees identical output to the target model only under greedy decoding. With temperature > 0, the token distribution may differ slightly.
• No training pipeline is included in the public repo. If you need to train a DART head for a new base model, you'll need to implement the training loop (supervised prediction of future tokens from intermediate hidden states) yourself.

Reference

Paper: DART: Diffusion-Inspired Speculative Decoding for Fast LLM Inference (Liu et al., 2026) -- Focus on Section 3 (method) for the parallel drafting mechanism and Section 3.3 for the N-gram-enforced tree pruning algorithm.

Code: https://github.com/fvliang/DART -- Apache 2.0 licensed, supports Qwen3 1.7B through 32B with pre-trained draft heads.