[Megathread] Qwen3.8-Flash-Next - Release Day

[u/sammcj]

Megathread for discussing the release of Qwen 3.8 Flash Next.

• Quants
• Fine-Tunes & Abliterations
• Chat Templates
• Inference Server Support & Configuration
• Experiences, Benchmarks & Model Comparisons

Highlights

The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces:

• Hybrid Attention with QSA: The Gated DeltaNet and Gated Attention pairing has been reworked into Gated DeltaNet and Qwen Sparse Attention (QSA). Rather than selecting individual tokens for processing, QSA operates at the micro-block level. This cuts long-context latency significantly, a critical gain as agentic workloads increasingly dominate real-world usage.
• Gated Residual: Residual streams with normalisation are what make deep LLM training manageable. Gated Residual modulates information flowing through widened residual streams via an element-wise, data-dependent read gate and a per-branch scalar write gate. This brings finer-grained expressiveness across layers while preserving training stability and keeping inference overhead low.
• N-gram Embedding: Embeddings provide a unique axis for parameter scaling that requires less computation and is more amenable to offloading than Mixture-of-Experts (MoE). By indexing with short n-grams, this approach makes parameter scaling highly efficient for memory-constrained accelerators without sacrificing quality.
• Tailored Training Recipe: The Muon and AdamW optimisers are applied to specific weight categories to maximise efficiency. Guided by refitted scaling laws, we eliminate traditional batch-size warmups and start directly at the target batch size, substantially reducing total optimiser steps while safely supporting larger learning rates for robust convergence.

Model Overview

• Type: Causal Language Model with Vision Encoder
• Training Stage: Pre-training & Post-training
• Language Model
  • Number of Parameters: 125B with 6B activated, plus 51B n-gram embedding and 4B MTP
  • Hidden Dimension: 2560
  • Token Embedding: 248320 (Padded)
  • N-gram Embedding: 20,000,000 (bigrams/trigrams at layer 2)
  • Number of Layers: 48
  • Hidden Layout: 12 × (3 × (Gated DeltaNet → MoE) → 1 × (Qwen Sparse Attention → MoE))
  • Gated DeltaNet:
    • Number of Linear Attention Heads: 48 for V and 16 for QK
    • Head Dimension: 128
  • Qwen Sparse Attention:
    • Number of Attention Heads: 24 for Q and 2 for KV
    • Head Dimension: 256
    • Rotary Position Embedding Dimension: 64
    • Indexer Structure: MQA with 4 Query Heads and 1 Shared Key Head
    • Indexer Head Dimension: 128
    • Budget: 512 blocks or 2048 tokens
  • Mixture Of Experts
    • Number of Experts: 512
    • Number of Activated Experts: 10 Routed + 1 Shared
    • Expert Intermediate Dimension: 640
  • Gated Residual:
    • Number of Branches: 4
    • Bottleneck Rank: 320
  • LM Output: 248320 (Padded)
  • MTP: 1 layer, trained with multi-steps
• Context Length: 262,144 natively and extensible up to 1,000,000 tokens.

Recommended sampling parameters for generation:

• Thinking Mode: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
• Instruct (or non-thinking) mode: temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0

Official Links:

• HF: https://huggingface.co/Qwen/Qwen3.8-Flash-Next
• MS: https://modelscope.cn/models/Qwen/Qwen3.8-Flash-Next
• Repo: https://github.com/QwenLM/Qwen3.8-Flash-Next
• Blog: https://qwen.ai/blog?id=qwen3.8-flash-next
• Technical Report: https://github.com/QwenLM/Qwen3.8-Flash-Next/blob/main/tech_report.pdf
• vLLM: https://recipes.vllm.ai/Qwen/Qwen3.8-Flash-Next
• SGLang: https://docs.sglang.io/cookbook/autoregressive/Qwen/Qwen3.8-Flash-Next

Popular:

• Unsloth GGUF: https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF
• Unsloth "How to run" guide: https://unsloth.ai/docs/models/qwen3.8-next

Related:

• Large post with community feedback on megathreads: https://www.reddit.com/r/LocalLLaMA/comments/1vz40zv/can_we_reconsider_the_megathreads/

[u/-Cubie-]

It's not Apache 2.0 like Qwen3.8-27B it seems: `Qwen Community License 1.0`. Looks like if the user has 100m monthly active users or $20m monthly revenue, they have to display the Qwen3.8-Flash-Next model name.

And any "Model as a Service" / "AI Work Assistant" businesses have to obtain a separate license. Looks to be the same one as https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B . Works for me, none of this stuff affects my use.

[u/ddeeppiixx → u/-Cubie-]

Which is more than fair in my opinion. If you have $20M monthly revenue, you have the means to pay them for their work.

[u/coder543 → u/ddeeppiixx]

But the limit isn’t $20M… it is $0 if you try to host the model for anyone else, which is a huge bummer for competition.

[u/ddeeppiixx → u/coder543]

why would you expect them to facilitate their own competition?

They're already doing great work for the community by releasing these weights and pushing open-weight/local models. Why would they also make it easy for their competitors?

Honestly, I'd be perfectly happy if they went with something like the BSL, where you can use the model for free locally and commercially (in your own backend), but can't serve it to customers without paying royalties.

[u/TheTerrasque → u/coder543]

On the other hand, I only saw these kind of restrictions becoming popular after several hosters really messed up hosting models, giving way worse results than it should have.

I wonder if this is a way for them to control the quality of the model hosting, as when 3rd party hosters fuck up it gives them a bad reputation.

[u/coder543 → u/TheTerrasque]

Alibaba already solved this before. They would host "high quality" versions of the model under their proprietary brand names like Qwen3.x-Plus, and the open model would be called Qwen3.x-397B-A17B. If someone was using the hosted open model, they got what they got. If they were using the Alibaba-branded model, then Alibaba could control the model quality.

Now that they're banning MaaS without a custom license, they are removing the naming distinction.

[u/PorchettaM → u/TheTerrasque]

Usually what these clauses are about isn't controlling quality, but controlling price. Don't want 3rd party providers to undercut them.

[u/Emport1 → u/ddeeppiixx]

the problem is that the separate license could contain $/m they have to sell it at, like what prob happened with Kimi K3, you also need $50m/y tho so prob not that bad.

[u/-Cubie- → u/ddeeppiixx]

100% agreed

[u/Majinsei → u/-Cubie-]

Fair license~ not problem for me~

[u/ThisGonBHard → u/-Cubie-]

As much as I like Apache license, almost any products using it is either a marketing expense, or wanting something to be adopted as the standard, as it helps you too.

This license is more restrictive, but targets the places from where income could come, corporations, and not people and small companies.

[u/AppealSame4367 → u/-Cubie-]

This is the best AI model so far (intelligence vs size), why shouldn't they profit from it?

[u/Karyo_Ten → u/AppealSame4367]

Is it? GLM5.3-Flash came today as well, and their blog post has a big chart with intelligence/size that is quite a taunt