DFlash 2
Qwen3.8-27B at 70 tok/s on an M5 Max MacBook Pro, which is up to 4.6× the speed of autoregressive decoding, with the same output.
Models: huggingface.co/collections/...
Blog: inco.ai/blog/dflash2/
Qwen3.8-27B at 70 tok/s on an M5 Max MacBook Pro, which is up to 4.6× the speed of autoregressive decoding, with the same output.
Models: huggingface.co/collections/...
Blog: inco.ai/blog/dflash2/
DFlash 2 - a incoai Collection
Keep Drafting Parallel
huggingface.co
August 19, 2026 at 3:59 AM
DFlash 2
Qwen3.8-27B at 70 tok/s on an M5 Max MacBook Pro, which is up to 4.6× the speed of autoregressive decoding, with the same output.
Models: huggingface.co/collections/...
Blog: inco.ai/blog/dflash2/
Qwen3.8-27B at 70 tok/s on an M5 Max MacBook Pro, which is up to 4.6× the speed of autoregressive decoding, with the same output.
Models: huggingface.co/collections/...
Blog: inco.ai/blog/dflash2/
DwarfStar with DFlash speculative decoding now can do the DeepSeek v4 Flash inference *much* faster both when used with Metal and the DGX Spark.
August 9, 2026 at 6:32 PM
DwarfStar with DFlash speculative decoding now can do the DeepSeek v4 Flash inference *much* faster both when used with Metal and the DGX Spark.
Dflashが進化した！Dflash-2を追いかける〜メインモデル/Dflashと速度・Acceptanceを比較してみた〜 | Yuichi Tominaga #zenn
https://zenn.dev/highreso/articles/2600bd34c59517
https://zenn.dev/highreso/articles/2600bd34c59517
Dflashが進化した！Dflash-2を追いかける〜メインモデル/Dflashと速度・Acceptanceを比較してみた〜
zenn.dev
September 24, 2026 at 6:49 AM
Dflashが進化した！Dflash-2を追いかける〜メインモデル/Dflashと速度・Acceptanceを比較してみた〜 | Yuichi Tominaga #zenn
https://zenn.dev/highreso/articles/2600bd34c59517
https://zenn.dev/highreso/articles/2600bd34c59517
This is really cool. This lab found a way to use diffusion for speculative decoding w/ a potential 10x speed up in inference speed
We still haven’t seen the extent of optimizations available for LLMs… current gen hardware is going to be able to run some amazing models in a year, I bet
We still haven’t seen the extent of optimizations available for LLMs… current gen hardware is going to be able to run some amazing models in a year, I bet
DFlash: Block Diffusion for Flash Speculative Decoding - Z Lab
DFlash: Block Diffusion for Flash Speculative Decoding. Jian Chen, Yesheng Liang, Zhijian Liu. Preprint.
z-lab.ai
April 9, 2026 at 2:27 AM
This is really cool. This lab found a way to use diffusion for speculative decoding w/ a potential 10x speed up in inference speed
We still haven’t seen the extent of optimizations available for LLMs… current gen hardware is going to be able to run some amazing models in a year, I bet
We still haven’t seen the extent of optimizations available for LLMs… current gen hardware is going to be able to run some amazing models in a year, I bet
trying out Qwen 3.6 35B A3B using DFlash on github.com/Neroued/ninfer, and it is capable of >500 decode TPS on code-like output - and 35B might actually be usable for writing some types of code, so I'm quite excited about this
August 12, 2026 at 12:25 PM
trying out Qwen 3.6 35B A3B using DFlash on github.com/Neroued/ninfer, and it is capable of >500 decode TPS on code-like output - and 35B might actually be usable for writing some types of code, so I'm quite excited about this
Run Gemma-4-12B-it-8bit x1.5-2.5 faster on #Apple M4 Pro with DSpark/DFlash.
#LLM #AI
#OpenSource MIT lic
github.com/ARahim3/mlx-...
#LLM #AI
#OpenSource MIT lic
github.com/ARahim3/mlx-...
GitHub - ARahim3/mlx-dspark: DeepSeek's DSpark and z-lab's DFlash speculative decoding — running natively on Apple Silicon via MLX.
DeepSeek's DSpark and z-lab's DFlash speculative decoding — running natively on Apple Silicon via MLX. - ARahim3/mlx-dspark
github.com
July 11, 2026 at 8:25 PM
Run Gemma-4-12B-it-8bit x1.5-2.5 faster on #Apple M4 Pro with DSpark/DFlash.
#LLM #AI
#OpenSource MIT lic
github.com/ARahim3/mlx-...
#LLM #AI
#OpenSource MIT lic
github.com/ARahim3/mlx-...
#Term: #Dflash #SpeculativeDecoding – #ArtificialIntelligence - https://with.ga/49kki
"DFlash is a speculative decoding technique that accelerates Large Language Model (#Llm) #Inference by generating blocks of tokens in parallel rather than sequentially. It replaces the traditional autoregre...
"DFlash is a speculative decoding technique that accelerates Large Language Model (#Llm) #Inference by generating blocks of tokens in parallel rather than sequentially. It replaces the traditional autoregre...
July 11, 2026 at 9:37 AM
#Term: #Dflash #SpeculativeDecoding – #ArtificialIntelligence - https://with.ga/49kki
"DFlash is a speculative decoding technique that accelerates Large Language Model (#Llm) #Inference by generating blocks of tokens in parallel rather than sequentially. It replaces the traditional autoregre...
"DFlash is a speculative decoding technique that accelerates Large Language Model (#Llm) #Inference by generating blocks of tokens in parallel rather than sequentially. It replaces the traditional autoregre...
#Term: #Dflash #SpeculativeDecoding – #ArtificialIntelligence - https://with.ga/49kki
"DFlash is a speculative decoding technique that accelerates Large Language Model (#Llm) #Inference by generating blocks of tokens in parallel rather than sequentially. It replaces the traditional autoregre...
"DFlash is a speculative decoding technique that accelerates Large Language Model (#Llm) #Inference by generating blocks of tokens in parallel rather than sequentially. It replaces the traditional autoregre...
July 11, 2026 at 9:30 AM
#Term: #Dflash #SpeculativeDecoding – #ArtificialIntelligence - https://with.ga/49kki
"DFlash is a speculative decoding technique that accelerates Large Language Model (#Llm) #Inference by generating blocks of tokens in parallel rather than sequentially. It replaces the traditional autoregre...
"DFlash is a speculative decoding technique that accelerates Large Language Model (#Llm) #Inference by generating blocks of tokens in parallel rather than sequentially. It replaces the traditional autoregre...
On NVIDIA Blackwell, DFlash delivers up to 15x higher throughput for 120B parameter models without application refactoring, drastically cutting latency for agentic operations. #nvidia #dflash #llm #opensource #agenticai #developers #ai #technology
NVIDIA: DFlash block diffusion accelerates autoregressive LLMs
Deploying DFlash block diffusion on NVIDIA hardware accelerates autoregressive LLMs during latency-sensitive inference.
www.developer-tech.com
June 24, 2026 at 4:03 PM
On NVIDIA Blackwell, DFlash delivers up to 15x higher throughput for 120B parameter models without application refactoring, drastically cutting latency for agentic operations. #nvidia #dflash #llm #opensource #agenticai #developers #ai #technology
DSpark speculative decoding is a tragedy for DeepSeek v4 Flash benchmarks. You can't trust anything, since it is too dependent on what you are generating (extreme case: count from 1 to 100). Always publish no Dflash numbers *as well* if you want to build trust.
August 14, 2026 at 10:57 AM
DSpark speculative decoding is a tragedy for DeepSeek v4 Flash benchmarks. You can't trust anything, since it is too dependent on what you are generating (extreme case: count from 1 to 100). Always publish no Dflash numbers *as well* if you want to build trust.
Muse Shimmer 30B
very cool that they’re launching it with speculative decoding instead of butchering the core model with MoE
research.meta.ai/blog/introdu...
very cool that they’re launching it with speculative decoding instead of butchering the core model with MoE
research.meta.ai/blog/introdu...
August 10, 2026 at 11:34 AM
Muse Shimmer 30B
very cool that they’re launching it with speculative decoding instead of butchering the core model with MoE
research.meta.ai/blog/introdu...
very cool that they’re launching it with speculative decoding instead of butchering the core model with MoE
research.meta.ai/blog/introdu...
This Week in Local AI — I Built DFlash and Audited Lightning
I built DFlash from source on a real RTX 3090 and benched both Qwens. Then audited my stack after PyPI's `lightning` package shipped malware that abuses Claude Code hooks.
#LocalAI
I built DFlash from source on a real RTX 3090 and benched both Qwens. Then audited my stack after PyPI's `lightning` package shipped malware that abuses Claude Code hooks.
#LocalAI
This Week in Local AI — I Built DFlash and Audited Lightning
I built DFlash from source on a real RTX 3090 and benched both Qwens. Then audited my stack after PyPI's `lightning` package shipped malware that abuses Claude Code hooks.
insiderllm.com
May 4, 2026 at 3:00 PM
This Week in Local AI — I Built DFlash and Audited Lightning
I built DFlash from source on a real RTX 3090 and benched both Qwens. Then audited my stack after PyPI's `lightning` package shipped malware that abuses Claude Code hooks.
#LocalAI
I built DFlash from source on a real RTX 3090 and benched both Qwens. Then audited my stack after PyPI's `lightning` package shipped malware that abuses Claude Code hooks.
#LocalAI
[PLACEHOLDER: punchy 200-char hot take after numbers come in. Lead with the surprising finding from the bench, not the title repeat.]
#LocalAI
#LocalAI
DFlash vs MTP on RTX 3090: I Tested Both Locally
Firsthand head-to-head bench of DFlash + DDTree against MTP (PR #22673) on a single RTX 3090, same Qwen 3.6-27B target. Real numbers, both backends.
insiderllm.com
May 6, 2026 at 7:00 PM
[PLACEHOLDER: punchy 200-char hot take after numbers come in. Lead with the surprising finding from the bench, not the title repeat.]
#LocalAI
#LocalAI
huggingface.co/unsloth/Lagu..., no DFlash (seems to be busted at this time), ik_llama.cpp, 2x3090 with ~12GB CPU offload
gets >1000 TPS prefill (nice!) and 30 TPS decode, so quite usable if I'm willing to be patient
if DFlash can be made to work, it might just push that into interactive territory
gets >1000 TPS prefill (nice!) and 30 TPS decode, so quite usable if I'm willing to be patient
if DFlash can be made to work, it might just push that into interactive territory
July 22, 2026 at 9:34 PM
huggingface.co/unsloth/Lagu..., no DFlash (seems to be busted at this time), ik_llama.cpp, 2x3090 with ~12GB CPU offload
gets >1000 TPS prefill (nice!) and 30 TPS decode, so quite usable if I'm willing to be patient
if DFlash can be made to work, it might just push that into interactive territory
gets >1000 TPS prefill (nice!) and 30 TPS decode, so quite usable if I'm willing to be patient
if DFlash can be made to work, it might just push that into interactive territory
Tencent's AngelSpec, an end-to-end speculative decoding framework supporting both training and deployment.
On Hy3-A21B, DFly delivers a 1.98–2.40× end-to-end speedup over autoregressive decoding across tested concurrency levels from 4 to 64, with 10.5–11.8% higher throughput than DFlash.
On Hy3-A21B, DFly delivers a 1.98–2.40× end-to-end speedup over autoregressive decoding across tested concurrency levels from 4 to 64, with 10.5–11.8% higher throughput than DFlash.
August 3, 2026 at 10:14 PM
Tencent's AngelSpec, an end-to-end speculative decoding framework supporting both training and deployment.
On Hy3-A21B, DFly delivers a 1.98–2.40× end-to-end speedup over autoregressive decoding across tested concurrency levels from 4 to 64, with 10.5–11.8% higher throughput than DFlash.
On Hy3-A21B, DFly delivers a 1.98–2.40× end-to-end speedup over autoregressive decoding across tested concurrency levels from 4 to 64, with 10.5–11.8% higher throughput than DFlash.
Handy share. The shipwithmuse.live glossary covers the terms around these files (GGUF, DFlash, contributor models) and notes Unsloth lists Glimmer GGUFs from 2-bit to 8-bit, useful for picking a quant: shipwithmuse.live/blog/muse-glossary
September 25, 2026 at 1:57 PM
Handy share. The shipwithmuse.live glossary covers the terms around these files (GGUF, DFlash, contributor models) and notes Unsloth lists Glimmer GGUFs from 2-bit to 8-bit, useful for picking a quant: shipwithmuse.live/blog/muse-glossary
Speculative decoding was already clever: small model drafts, big model verifies.
DFlash makes the draft stage parallel with block diffusion, so the model stops crawling one token at a time.
My review:
bit.ly/4ufj2UU
#AI #LLM #MachineLearning
DFlash makes the draft stage parallel with block diffusion, so the model stops crawling one token at a time.
My review:
bit.ly/4ufj2UU
#AI #LLM #MachineLearning
DFlash: The Trick That Makes LLMs Stop Crawling One Token at a Time
Speculative decoding was already clever. DFlash makes the draft stage parallel, turning diffusion from a clumsy text generator into a very…
bit.ly
May 15, 2026 at 4:27 PM
Speculative decoding was already clever: small model drafts, big model verifies.
DFlash makes the draft stage parallel with block diffusion, so the model stops crawling one token at a time.
My review:
bit.ly/4ufj2UU
#AI #LLM #MachineLearning
DFlash makes the draft stage parallel with block diffusion, so the model stops crawling one token at a time.
My review:
bit.ly/4ufj2UU
#AI #LLM #MachineLearning
Good summary. For anyone deciding how to actually run it, shipwithmuse.live keeps about 190 entries on local Glimmer: GGUF and MLX quants, DFlash speculative decoding, GPU benchmarks, Mac setups and fine-tunes: shipwithmuse.live/categories/local-and-open-models
September 25, 2026 at 1:56 PM
Good summary. For anyone deciding how to actually run it, shipwithmuse.live keeps about 190 entries on local Glimmer: GGUF and MLX quants, DFlash speculative decoding, GPU benchmarks, Mac setups and fine-tunes: shipwithmuse.live/categories/local-and-open-models
Speculative Decoding is the coolest trick for speeding up LLM inference!
Check out the video and learn why rejection sampling preserves quality, and how methods such as draft trees, Medusa, MTP, EAGLE, and DFlash further accelerate LLM inference.
youtu.be/l8gWQlrVOKQ
Check out the video and learn why rejection sampling preserves quality, and how methods such as draft trees, Medusa, MTP, EAGLE, and DFlash further accelerate LLM inference.
youtu.be/l8gWQlrVOKQ
September 1, 2026 at 11:26 PM
Speculative Decoding is the coolest trick for speeding up LLM inference!
Check out the video and learn why rejection sampling preserves quality, and how methods such as draft trees, Medusa, MTP, EAGLE, and DFlash further accelerate LLM inference.
youtu.be/l8gWQlrVOKQ
Check out the video and learn why rejection sampling preserves quality, and how methods such as draft trees, Medusa, MTP, EAGLE, and DFlash further accelerate LLM inference.
youtu.be/l8gWQlrVOKQ
The thinking-in-circles thing seems to be the common complaint with Glimmer. shipwithmuse.live keeps 187 entries on running it locally, covering GGUF and MLX quants, DFlash speculative decoding and GPU benchmarks, if you want to compare setups: shipwithmuse.live/categories/local-and-open-models
September 25, 2026 at 11:47 AM
The thinking-in-circles thing seems to be the common complaint with Glimmer. shipwithmuse.live keeps 187 entries on running it locally, covering GGUF and MLX quants, DFlash speculative decoding and GPU benchmarks, if you want to compare setups: shipwithmuse.live/categories/local-and-open-models
Long think-before-code on big repos is a real weak spot. If you give it another go, shipwithmuse.live lists local Glimmer setups side by side, including different GGUF/MLX quants and DFlash speculative decoding, to compare against yours: shipwithmuse.live/categories/local-and-open-models
September 25, 2026 at 12:17 PM
Long think-before-code on big repos is a real weak spot. If you give it another go, shipwithmuse.live lists local Glimmer setups side by side, including different GGUF/MLX quants and DFlash speculative decoding, to compare against yours: shipwithmuse.live/categories/local-and-open-models
Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash
towardsdatascience.com/speculative-...
towardsdatascience.com/speculative-...
Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash | Towards Data Science
How DFlash trades spare compute for saved memory bandwidth, and why its gains shrink as concurrency rises
towardsdatascience.com
August 26, 2026 at 12:26 AM
Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash
towardsdatascience.com/speculative-...
towardsdatascience.com/speculative-...
MEDC17 DFLASH Checksum Correction Utility. Pro software for Bosch EDC17 and MED17 ECU data flash repair. Recalculates EEPROM checksums for IMMO edits, ECU cloning, and module adaptation workflows. Technical Tricore tool. Link: binunlock.com/t/medc17-dfl... #MEDC17 #Tricore #EEPROM #binunlock
MEDC17 DFlash Checksum Correction Utility by MasterB8r
Hey all, I made a program for correcting the DFlash/EEPROM checksums for the MEDC17 family of ECUs, which can be used to patch the file after changing VIN,…
binunlock.com
May 23, 2026 at 8:31 PM
MEDC17 DFLASH Checksum Correction Utility. Pro software for Bosch EDC17 and MED17 ECU data flash repair. Recalculates EEPROM checksums for IMMO edits, ECU cloning, and module adaptation workflows. Technical Tricore tool. Link: binunlock.com/t/medc17-dfl... #MEDC17 #Tricore #EEPROM #binunlock
S2E2 of Papers In The Park!
This week: DFlash: Block Diffusion for Flash Speculative Decoding
arxiv.org/abs/2602.06036
This week: DFlash: Block Diffusion for Flash Speculative Decoding
arxiv.org/abs/2602.06036
May 9, 2026 at 4:46 PM
S2E2 of Papers In The Park!
This week: DFlash: Block Diffusion for Flash Speculative Decoding
arxiv.org/abs/2602.06036
This week: DFlash: Block Diffusion for Flash Speculative Decoding
arxiv.org/abs/2602.06036
Meta just entered the game in the local Agentic world: Muse Glimmer, 30b dense model that run on 20GB of vram. Dflash ready.
Can't wait to benchmark it against Qwen3.8 27b!
Can't wait to benchmark it against Qwen3.8 27b!
August 10, 2026 at 12:15 PM
Meta just entered the game in the local Agentic world: Muse Glimmer, 30b dense model that run on 20GB of vram. Dflash ready.
Can't wait to benchmark it against Qwen3.8 27b!
Can't wait to benchmark it against Qwen3.8 27b!