Qwen3.6-35B-A3B-DFlash
Qwen3.6-35B-A3B-DFlash is a specialized 35B-parameter draft model designed for speculative decoding—it is not a standalone LLM. It works alongside Qwen3.6-35B-A3B to propose multiple tokens in parallel using block diffusion, which the target model then verifies. This approach aims to accelerate inference throughput (up to 3.61x speedup at concurrency 1) without changing the output distribution. It requires SGLang or vLLM (draft PR) and is licensed under Apache 2.0.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | z-lab |
| Parameters | 386M |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 182.9k |
| Likes | 260 |
| Last updated | 2026-06-19 |
| Source | z-lab/Qwen3.6-35B-A3B-DFlash |
What Qwen3.6-35B-A3B-DFlash is
A 35B diffusion-based draft model trained by Z-Lab and Modal on 40k sequence length with sliding-window attention. Deployed as part of a speculative decoding pipeline where it generates token proposals that are verified by the target Qwen3.6-35B-A3B model. Benchmarks show 2.89x–3.61x throughput gains across GSM8K, MATH500, HumanEval, MBPP, and MT-Bench at varying concurrency levels on 1x NVIDIA B200 GPU.
Run Qwen3.6-35B-A3B-DFlash locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="z-lab/Qwen3.6-35B-A3B-DFlash")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
ESTIMATE: ~85–90 GB total VRAM for both draft (DFlash, ~35B params, bfloat16 ~70 GB) + target (Qwen3.6-35B-A3B, ~35B params, bfloat16 ~70 GB) on a single GPU. Benchmarks run on 1x NVIDIA B200; H100 (80GB) will fit with tensor parallelism (tp-size > 1) or offloading. Requires CUDA 12.0+, recent SGLang build, and FlashAttention/TRT-LLM backends for optimal performance.
DFlash is a pretrained draft model and Not explicitly positioned for fine-tuning. It is a diffusion model coupled to a specific target model architecture. Fine-tuning or LoRA adaptation is not documented. To speculate on other domains, retrain from scratch or use the base Qwen3.6-35B-A3B; modifying DFlash alone may break speculative decoding alignment. Requires review with Z-Lab or Modal for customization guidance.
When to avoid it — and what to weigh
- You Need a Standalone Language Model — DFlash is a draft model only; it cannot generate text independently. You must pair it with the full Qwen3.6-35B-A3B target model, doubling memory overhead and complexity.
- You Require Sub-10ms Token Latency — Speculative decoding introduces verification overhead. Single-token latency may not improve or may increase compared to direct inference; prioritize throughput-optimized serving.
- You Need vLLM as Your Sole Inference Backend — vLLM support is in draft PR status (vllm-project/vllm#40898) and not yet merged. SGLang is the primary validated backend; vLLM support is not production-ready.
- Your Target Model is Not Qwen3.6-35B-A3B — This model is tuned specifically for Qwen3.6-35B-A3B. Pairing with other target models is unsupported and accept lengths / speedup may degrade significantly.
License & commercial use
Apache License 2.0 (SPDX: apache-2.0). This is a permissive OSI-approved open-source license.
Apache 2.0 permits commercial use, modification, and distribution under the same license with attribution. No restrictions on commercial deployment stated in the license. However, verify with Z-Lab and Modal that use of their jointly trained model aligns with their terms of service and any supporting compute/cloud agreements. Requires review if you plan to resell or embed in a commercial product without modification.
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 | High |
| DEV.co fit | Good |
| Assessment confidence | High |
DFlash inherits the security posture of Qwen3.6-35B-A3B and the SGLang/vLLM inference servers. Custom code is enabled (tag: custom_code). Verify that SGLang and vLLM builds are up-to-date and sourced from official channels. Run DFlash in isolated environments or VPCs; monitor GPU memory and CPU/network usage for unexpected load. No formal security audit or threat model is stated. Treat similarly to any large model serving infrastructure.
Alternatives to consider
Qwen's Built-in MTP Draft Path
Native multi-token prediction within Qwen3.6-35B-A3B. Avoids dual-model overhead but typically achieves lower speedups (1.5–2.2x per benchmarks) and worse accept lengths than DFlash block=8/16.
Medusa (Speculative Decoding)
Another draft model approach that grows a lightweight tree of predictions. Simpler training than diffusion, but requires Medusa-specific checkpoints and integration. May not outperform DFlash on this target.
Llama 3.1-70B or Mistral-Large with Batching
Larger standalone models with native throughput via batching. Avoid dual-model complexity but require more VRAM; benchmarks not directly comparable. Consider if you need a general-purpose 70B+ model.
Ship Qwen3.6-35B-A3B-DFlash with senior software developers
Integrate DFlash with SGLang to accelerate Qwen inference on your GPU infrastructure. Review our deployment guide, benchmark your target workloads, and verify hardware requirements with our team.
Talk to DEV.coRelated open-source tools
Surfaced by semantic similarity across the DEV.co open-source index.
Related on DEV.co
Explore the category and the services that help you build with it.
Qwen3.6-35B-A3B-DFlash FAQ
Can I use DFlash for commercial inference without contacting Z-Lab?
What GPU do I need to run DFlash + target model?
Does DFlash work with other target models besides Qwen3.6-35B-A3B?
Is vLLM support ready for production?
Work with a software development agency
DEV.co helps companies turn open-source tools like Qwen3.6-35B-A3B-DFlash into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source llms stack.
Ready to Deploy High-Throughput LLM Inference?
Integrate DFlash with SGLang to accelerate Qwen inference on your GPU infrastructure. Review our deployment guide, benchmark your target workloads, and verify hardware requirements with our team.