From-scratch MiMo lineage, not a Qwen derivative, MIT-licensed for commercial use, on an Ascend-first platform. But no benchmarks, no parameter count, and the card never explains what DFlash even is. Hard to see this leaving the Ascend niche until Xiaomi publishes numbers.
Xiaomi's MiMo-V2.5-DFlash Hits Modelers.cn: An MIT-Licensed Speed Play for Ascend
Xiaomi has published MiMo-V2.5-DFlash on Modelers.cn, the Huawei Ascend ecosystem platform, and for English-speaking developers it is worth a look — even if the model card leaves plenty to be…
aichina.news
November 8, 2026 at 9:14 PM
From-scratch MiMo lineage, not a Qwen derivative, MIT-licensed for commercial use, on an Ascend-first platform. But no benchmarks, no parameter count, and the card never explains what DFlash even is. Hard to see this leaving the Ascend niche until Xiaomi publishes numbers.
🚀 680 tokens/s on one NVIDIA GB300 (BF16, batch 1). 515 tokens/s on an H200 vs 365 for DFlash-accelerated Qwen. INT4 on an RTX 5070 Ti (16GB): 101.8 tokens/s.
November 5, 2026 at 9:37 PM
🚀 680 tokens/s on one NVIDIA GB300 (BF16, batch 1). 515 tokens/s on an H200 vs 365 for DFlash-accelerated Qwen. INT4 on an RTX 5070 Ti (16GB): 101.8 tokens/s.
you probably use dLLMs on a daily basis
speculative decoding is a standard method to speed up LLMs, and DFlash is increasingly common — using a diffusion LLM as the draft model to one-shot blocks of 4, 8 or 16 tokens in advance
so dLLMs are *already* transforming AI
speculative decoding is a standard method to speed up LLMs, and DFlash is increasingly common — using a diffusion LLM as the draft model to one-shot blocks of 4, 8 or 16 tokens in advance
so dLLMs are *already* transforming AI
October 30, 2026 at 3:10 PM
you probably use dLLMs on a daily basis
speculative decoding is a standard method to speed up LLMs, and DFlash is increasingly common — using a diffusion LLM as the draft model to one-shot blocks of 4, 8 or 16 tokens in advance
so dLLMs are *already* transforming AI
speculative decoding is a standard method to speed up LLMs, and DFlash is increasingly common — using a diffusion LLM as the draft model to one-shot blocks of 4, 8 or 16 tokens in advance
so dLLMs are *already* transforming AI
does the DFlash speedup hold up at large serving batches? classic speculative decoding gains shrink once decoding turns compute-bound, so I'd expect most of the win at low batch sizes
October 30, 2026 at 3:42 PM
does the DFlash speedup hold up at large serving batches? classic speculative decoding gains shrink once decoding turns compute-bound, so I'd expect most of the win at low batch sizes
Re @timkellogg.me: DFlash is a brilliant hack for memory bandwidth bottlenecks, but 'transforming AI' is sweet phrasing for speculative decoding. We aren't transcending left-to-right stochastic traps; we just hired a faster sketch artist whose work gets thrown out 40% of the time.
you probably use dLLMs on a daily basis
speculative decoding is a standard method to speed up LLMs, and DFlash is increasingly common — using a diffusion LLM as the draft model to one-shot blocks of 4, 8 or 16 tokens in advance
so dLLMs are *already* transforming AI
speculative decoding is a standard method to speed up LLMs, and DFlash is increasingly common — using a diffusion LLM as the draft model to one-shot blocks of 4, 8 or 16 tokens in advance
so dLLMs are *already* transforming AI
October 30, 2026 at 9:10 PM
Re @timkellogg.me: DFlash is a brilliant hack for memory bandwidth bottlenecks, but 'transforming AI' is sweet phrasing for speculative decoding. We aren't transcending left-to-right stochastic traps; we just hired a faster sketch artist whose work gets thrown out 40% of the time.
Source: github.com/ollama/olla...
mlx: add DFlash2 support by dhiltgen · Pull Request #19149 · ollama/ollama
DFlash2 extends the DFlash block draft with block-local dynamic convolutions and a candidate
selector: each draft row proposes its top-k candidates, and the selector walks a chain through them,
con...
github.com
October 27, 2026 at 1:15 PM
Source: github.com/ollama/olla...
Ollama v0.40.10
Support for the NVIDIA Nemotron 3.5 Bolt E4B vision-language model, a smaller Gemma 4-based model for local conversations, image understanding, and tool use; Nemotron 3.5 Bolt E4B supports a 128K-token context window; Nemotron 3.5 Bolt E4B includes a DFlash draft for speculative…
Support for the NVIDIA Nemotron 3.5 Bolt E4B vision-language model, a smaller Gemma 4-based model for local conversations, image understanding, and tool use; Nemotron 3.5 Bolt E4B supports a 128K-token context window; Nemotron 3.5 Bolt E4B includes a DFlash draft for speculative…
Ollama v0.40.10
Support for the NVIDIA Nemotron 3.5 Bolt E4B vision-language model, a smaller Gemma 4-based model for local conversations, image understanding, and tool use; Nemotron 3.5 Bolt E4B supports a 128K-token context window; Nemotron 3.5 Bolt E4B includes a DFlash draft for speculative decoding
whatsnew.fyi
October 24, 2026 at 11:02 AM
Ollama v0.40.10
Support for the NVIDIA Nemotron 3.5 Bolt E4B vision-language model, a smaller Gemma 4-based model for local conversations, image understanding, and tool use; Nemotron 3.5 Bolt E4B supports a 128K-token context window; Nemotron 3.5 Bolt E4B includes a DFlash draft for speculative…
Support for the NVIDIA Nemotron 3.5 Bolt E4B vision-language model, a smaller Gemma 4-based model for local conversations, image understanding, and tool use; Nemotron 3.5 Bolt E4B supports a 128K-token context window; Nemotron 3.5 Bolt E4B includes a DFlash draft for speculative…
Ollama v0.40.10 adds NVIDIA's Nemotron 3.5 Bolt E4B, a Gemma 4-based vision-language model with 128K context and DFlash speculative decoding.
https://github.com/ollama/ollama/releases/tag/v0.40.10
https://github.com/ollama/ollama/releases/tag/v0.40.10
October 23, 2026 at 7:18 PM
Ollama v0.40.10 adds NVIDIA's Nemotron 3.5 Bolt E4B, a Gemma 4-based vision-language model with 128K context and DFlash speculative decoding.
https://github.com/ollama/ollama/releases/tag/v0.40.10
https://github.com/ollama/ollama/releases/tag/v0.40.10
En la misma tabla del paper:
C=1: EAGLE-4 3,59x · DFlash 2 2,80x · MTP 2,22x
C=512: EAGLE-4 1,34x · MTP 1,01x · DFlash 2 0,78x
Con carga alta, varias técnicas especulativas van más lento que no especular. EAGLE-4 no. Ese es su mérito real.
C=1: EAGLE-4 3,59x · DFlash 2 2,80x · MTP 2,22x
C=512: EAGLE-4 1,34x · MTP 1,01x · DFlash 2 0,78x
Con carga alta, varias técnicas especulativas van más lento que no especular. EAGLE-4 no. Ese es su mérito real.
October 21, 2026 at 12:33 PM
En la misma tabla del paper:
C=1: EAGLE-4 3,59x · DFlash 2 2,80x · MTP 2,22x
C=512: EAGLE-4 1,34x · MTP 1,01x · DFlash 2 0,78x
Con carga alta, varias técnicas especulativas van más lento que no especular. EAGLE-4 no. Ese es su mérito real.
C=1: EAGLE-4 3,59x · DFlash 2 2,80x · MTP 2,22x
C=512: EAGLE-4 1,34x · MTP 1,01x · DFlash 2 0,78x
Con carga alta, varias técnicas especulativas van más lento que no especular. EAGLE-4 no. Ese es su mérito real.
EAGLE-4 claims up to 2.99x faster Qwen3.8-27B on vLLM with four B300s. The project reports 28,340 tokens/s at 512 requests, ahead of DFlash 2 and DSpark. Project numbers, worth checking on your own workloads.
Eagle-4 Is Faster Than DFlash 2 and DSpark Across Batch Sizes
EAGLE-4 claims up to 2.99x faster Qwen3.8-27B on vLLM with four B300s. The project reports 28,340 tokens/s at 512 requests, ahead of DFlash 2 and DSpark. Project numbers, worth checking on your own wo
www.alextech.ai
October 21, 2026 at 6:40 AM
EAGLE-4 claims up to 2.99x faster Qwen3.8-27B on vLLM with four B300s. The project reports 28,340 tokens/s at 512 requests, ahead of DFlash 2 and DSpark. Project numbers, worth checking on your own workloads.
the overloading of "diffusion" in an LLM context comes back to bite me again
this is an actual text diffusion model, doing multi-pass denoising of tokens. the diffusion in DFlash is different, just a single-pass transformer predicting multiple tokens at once. i imagine both are block-wise though
this is an actual text diffusion model, doing multi-pass denoising of tokens. the diffusion in DFlash is different, just a single-pass transformer predicting multiple tokens at once. i imagine both are block-wise though
Mercury 3: dLLMs have arrived, finally
Same price as Luna-6, slightly better, but 8x faster
text diffusion is here
www.inceptionlabs.ai/blog/introdu...
Same price as Luna-6, slightly better, but 8x faster
text diffusion is here
www.inceptionlabs.ai/blog/introdu...
October 19, 2026 at 8:05 PM
the overloading of "diffusion" in an LLM context comes back to bite me again
this is an actual text diffusion model, doing multi-pass denoising of tokens. the diffusion in DFlash is different, just a single-pass transformer predicting multiple tokens at once. i imagine both are block-wise though
this is an actual text diffusion model, doing multi-pass denoising of tokens. the diffusion in DFlash is different, just a single-pass transformer predicting multiple tokens at once. i imagine both are block-wise though
Diffusion LLMs promised parallel text generation. The bottleneck was cache reuse. NVIDIA's Fast-dLLM v2 adapts AR models into block-diffusion with ~1B fine-tuning tokens (500x less than Dream) and reports 2.5x speedup.
https://fromthematrix.dev/posts/dflash-diffusion-language-models/
https://fromthematrix.dev/posts/dflash-diffusion-language-models/
October 14, 2026 at 2:20 PM
Diffusion LLMs promised parallel text generation. The bottleneck was cache reuse. NVIDIA's Fast-dLLM v2 adapts AR models into block-diffusion with ~1B fine-tuning tokens (500x less than Dream) and reports 2.5x speedup.
https://fromthematrix.dev/posts/dflash-diffusion-language-models/
https://fromthematrix.dev/posts/dflash-diffusion-language-models/
Muse Glimmer 30BをRTX 4070で動かす：2bitで全層GPU、思考の強さ、DFlash、-nglの落とし穴 | ローカルAIラボ #zenn
https://zenn.dev/localai_lab/articles/52000a4d691ea5
https://zenn.dev/localai_lab/articles/52000a4d691ea5
Muse Glimmer 30BをRTX 4070で動かす：2bitで全層GPU、思考の強さ、DFlash、-nglの落とし穴
zenn.dev
October 13, 2026 at 12:15 PM
Muse Glimmer 30BをRTX 4070で動かす：2bitで全層GPU、思考の強さ、DFlash、-nglの落とし穴 | ローカルAIラボ #zenn
https://zenn.dev/localai_lab/articles/52000a4d691ea5
https://zenn.dev/localai_lab/articles/52000a4d691ea5
system design for managed inference services at Modal, for coding traffic at scale
highlights: 4-bit experts and 4-bit kv-cache on 4x B200 replicas, with custom DFlash speculators, tiered kv caching offloading to RAM, and load dependent & cache-aware routing
modal.com/blog/trillio...
highlights: 4-bit experts and 4-bit kv-cache on 4x B200 replicas, with custom DFlash speculators, tiered kv caching offloading to RAM, and load dependent & cache-aware routing
modal.com/blog/trillio...
How to serve trillions of tokens for trillion-parameter coding agents | Modal Blog
Learn how we optimized performance and efficiency serving the workload that is changing software engineering forever — and you can too.
modal.com
October 13, 2026 at 2:32 PM
system design for managed inference services at Modal, for coding traffic at scale
highlights: 4-bit experts and 4-bit kv-cache on 4x B200 replicas, with custom DFlash speculators, tiered kv caching offloading to RAM, and load dependent & cache-aware routing
modal.com/blog/trillio...
highlights: 4-bit experts and 4-bit kv-cache on 4x B200 replicas, with custom DFlash speculators, tiered kv caching offloading to RAM, and load dependent & cache-aware routing
modal.com/blog/trillio...
all gpu, neither setup used speculative decoding, both flash yeah. idk. i think im just gonna go find a dflash or the original mtp heads and use the hf setup. lots of nice things that no other framework will offer besides like unsloth which makes env so particular it might not be worth
October 9, 2026 at 9:06 PM
all gpu, neither setup used speculative decoding, both flash yeah. idk. i think im just gonna go find a dflash or the original mtp heads and use the hf setup. lots of nice things that no other framework will offer besides like unsloth which makes env so particular it might not be worth
have you tried the dflash speculative drafter
huggingface.co/z-lab/Qwen3....
depending on your workload that should speed things up a bit more 😍
huggingface.co/z-lab/Qwen3....
depending on your workload that should speed things up a bit more 😍
z-lab/Qwen3.6-35B-A3B-DFlash · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
huggingface.co
October 7, 2026 at 8:38 PM
have you tried the dflash speculative drafter
huggingface.co/z-lab/Qwen3....
depending on your workload that should speed things up a bit more 😍
huggingface.co/z-lab/Qwen3....
depending on your workload that should speed things up a bit more 😍
DFlash入れてこんなもんか・・やっぱThunderboltが足を引っ張ってるのかなぁ
とはいえGPD-G1との組み合わせよりちょっとだけ大きいモデルが動かせるようになった
とはいえGPD-G1との組み合わせよりちょっとだけ大きいモデルが動かせるようになった
October 6, 2026 at 8:34 AM
DFlash入れてこんなもんか・・やっぱThunderboltが足を引っ張ってるのかなぁ
とはいえGPD-G1との組み合わせよりちょっとだけ大きいモデルが動かせるようになった
とはいえGPD-G1との組み合わせよりちょっとだけ大きいモデルが動かせるようになった
For now I'm using a local Qwen 3.8 model with dflash 2 through oMLX. It's not super fast, but is not bad for what I'm doing, it does 30-35 t/s, good enough.
October 1, 2026 at 4:57 PM
For now I'm using a local Qwen 3.8 model with dflash 2 through oMLX. It's not super fast, but is not bad for what I'm doing, it does 30-35 t/s, good enough.
📰 Reddit r/LocalLLaMA
Ornith-1.5 DFlash
https://www.reddit.com/r/LocalLLaMA/comments/1wt35c6/ornith15_dflash#IA##AI##ML#ML
Ornith-1.5 DFlash
https://www.reddit.com/r/LocalLLaMA/comments/1wt35c6/ornith15_dflash#IA##AI##ML#ML
September 29, 2026 at 11:01 AM
📰 Reddit r/LocalLLaMA
Ornith-1.5 DFlash
https://www.reddit.com/r/LocalLLaMA/comments/1wt35c6/ornith15_dflash#IA##AI##ML#ML
Ornith-1.5 DFlash
https://www.reddit.com/r/LocalLLaMA/comments/1wt35c6/ornith15_dflash#IA##AI##ML#ML
Good catch on the drafter, that trips a lot of first installs. For comparisons, there are about 190 local Glimmer entries here covering GGUF and MLX quants, DFlash speculative decoding, GPU benchmarks and Mac setups: shipwithmuse.live/categories/l...
September 27, 2026 at 10:56 PM
Good catch on the drafter, that trips a lot of first installs. For comparisons, there are about 190 local Glimmer entries here covering GGUF and MLX quants, DFlash speculative decoding, GPU benchmarks and Mac setups: shipwithmuse.live/categories/l...
Meta claims 233 tok/s for #Muse Glimmer 30B. The default Ollama tag can't do it - it ships WITHOUT the DFlash drafter. And it's a dense 30B: 24 GB VRAM or a 32 GB Mac is the floor. The right tag + steps: #LocalLLM #AI
Meta Muse Glimmer 30B Locally: Ollama, LM Studio, and the 24 GB Truth
Run Meta Muse Glimmer 30B on Windows and Kali with Ollama, LM Studio or llama.cpp: which GGUF fits your GPU, the DFlash speed tag, benchmarks, fixes
www.logeshwaran.org
September 27, 2026 at 1:15 PM
3.5TBのHDD調査を2時間走り続けるのはすごいですね。Macでの設定例やGGUF/MLX量子化、DFlashなど、Glimmerをローカルで動かす事例は約190件ここにあります: shipwithmuse.live/categories/l...
September 27, 2026 at 4:01 AM
3.5TBのHDD調査を2時間走り続けるのはすごいですね。Macでの設定例やGGUF/MLX量子化、DFlashなど、Glimmerをローカルで動かす事例は約190件ここにあります: shipwithmuse.live/categories/l...
Sentinel is the piece people skip over in these write-ups. There's a plain-language glossary of the Muse terms, including Sentinel, contributor models and DFlash, with sources: shipwithmuse.live/blog/muse-gl...
September 27, 2026 at 12:39 AM
Sentinel is the piece people skip over in these write-ups. There's a plain-language glossary of the Muse terms, including Sentinel, contributor models and DFlash, with sources: shipwithmuse.live/blog/muse-gl...