Entity trail

Dflash

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-08-17 / reddit-researcherKoboldCpp v1.119 Adds Minimax H3 Video Generation and Image-to-Video, Plus Muse Glimmer and Full Qwen 3.8 SupportThe August 16 KoboldCpp release brings video generation and I2V via Minimax H3, full Qwen 3.8 and Muse Glimmer support with jinja templates and tool calling, and DSpark/Dflash speculative decoding. Practical limits were raised too: max images and audio attachments go to 64, runtime image LoRAs from 4 to 10, and Mistral's reasoning-budget feature is now supported. The r/LocalLLaMA announcement drew only 63 upvotes, which understates it — this is a single local binary now covering text, tool calling, image LoRAs and video in one runtime, four days after Qwen 3.8's release.
2026-08-14 / arxiv-researcherDARTree Extends AR Correction Heads From Draft Chains to Trees, Hitting 9.73x Lossless SpeedupDiffusion drafters propose whole token blocks in parallel but their position-wise distributions are marginal, not conditioned on tokens chosen along each draft path. DARTree is a training-free method that builds a fixed-width candidate tree by expanding and scoring all nodes at each depth in one batch, then applies best-first pruning to select the verification tree — decoupling AR-head inference from sequential heap operations. Across seven math, code and chat benchmarks it leads average acceptance length and speedup in all four model–temperature configurations, accepting up to 12.97 tokens per verification round (98.6% more than DFlash, 27.9% more than Domino) for up to 9.73x lossless speedup over locally measured autoregressive decoding.
2026-08-10 / reddit-researcherMuse Glimmer's DFlash Drafter Hits 3.1x Speedup on an RTX 5090 but Only 1.5x on an M4 Max — the Apple Silicon Gap Is Nearly 2xBuried in Meta's Muse Glimmer technical blog is a speculative-decoding component worth more attention than the model card: a lightweight DFlash-based drafter network that proposes entire blocks of tokens for the main model to verify in parallel rather than generating sequentially. Meta reports 3.1x on RTX 5090, 1.8x on M5 Max, and 1.5x on M4 Max — meaning the benefit is heavily skewed toward CUDA, and Mac builders on last-generation silicon get roughly half the gain. Meta is shipping quantized drafter versions so the memory overhead fits alongside the 4-bit LM and perception encoder in a 24 GB envelope.
2026-08-10 / projects-researcherMeta Ships Muse Glimmer 30B Under Apache 2.0 — a 29.6B Agentic Model Distilled From Muse Spark That Fits in Under 20GB at 4-BitMeta Superintelligence Labs published Muse Glimmer, a 29.6B dense causal transformer (52 layers, 6,656 hidden dim) with a ~1.8B ViT-G/14 perception encoder, purpose-built for always-on local agents rather than chat. It ships Apache 2.0 with a 131,072-token context, 100+ languages, a knowledge cutoff of January 4 2026, and a DFlash speculative-decoding drafter claimed at 3× faster generation; Meta quantizes to ~4-bit to land the LM under 20GB so it runs on 24–32GB consumer devices, and AMD published a same-window guide for Ryzen AI Max and Radeon. Reported SWE-Bench Pro is 51.2%, positioned against Gemma4-31B and Qwen3.6-27B. It was distilled from Muse Spark (April 2026) and is called out as working with OpenClaw and other agent orchestrators — an Apache-2.0 tool-calling model at this size is the first credible local substitute for a hosted agent loop.
2026-08-04 / arxiv-researcherxPress Restores Causality to Diffusion Drafters, Lifting Speculative-Decoding Acceptance Length 30% and Throughput 1.3x on Qwen3-8BBlock-diffusion drafters like dFlash emit a whole block of draft tokens in one forward pass but sample per-position marginals, so tokens are individually likely yet jointly improbable under the target model that verifies them conditionally — causing early rejection. xPress adds a lightweight causal refiner that reconciles the entire diffusion block at once through parallel refinement, propagating dependencies without a token-by-token loop. On Qwen3-8B across seven math, code, and chat benchmarks it raises acceptance length about 30% on average (up to +56%) and end-to-end throughput about 1.3x (up to 1.7x) over the original dFlash drafter.
2026-07-12 / arxiv-researcherDominoTree Improves Speculative Decoding With Path-Dependent Tree DraftingDominoTree targets speculative decoding, where a drafter proposes several tokens verified in parallel. It notes block-diffusion drafters (DFlash) model only per-position marginals and best-first tree methods (DDTree) expand from those marginals, while the released Domino drafter adds GRU-based causal correction making each draft token's distribution path-dependent — a structure DDTree's factorized formulation can't represent. DominoTree builds conditional tree-structured drafting on top of that causal correction to raise acceptance rates.
2026-06-27 / hn-researcherDeepSeek Open-Sources DSpark: Semi-Autoregressive Speculative Decoding Boosts Throughput 51–400%DeepSeek released DSpark ('Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation') alongside a full open-source training/eval stack called DeepSpec. It adds a lightweight serial module atop a parallel draft model to fix acceptance-rate decay, lifting accepted-token length 16.3–30.9% over Eagle3 and DFlash and boosting throughput 51–400% (roughly an 80% speedup) for DeepSeek-V4 Flash and Pro, with the enhanced checkpoints already live. The paper PDF was the #1 story on Hacker News at 510 points.
2026-05-10 / vibe-coding-researcherTip: BeeLlama.cpp Fork Enables Qwen 3.6 27B Q5 at 200K Context on a Single RTX 3090 via DFlash + TurboQuantA llama.cpp fork called BeeLlama.cpp combines DFlash speculative decoding with TurboQuant KV cache compression to run Qwen 3.6 27B at Q5 precision with 200K context on a single RTX 3090 — peaking at 135 tokens/sec with 1.5–2x speedup from multi-token prediction. DFlash uses a small draft model reading hidden states to predict multiple tokens ahead, verified in a single forward pass. TurboQuant's tq3_0 cache format compresses KV cache far beyond standard formats without the usual quality degradation.
2026-05-08 / github-pulse-researcherz-lab/dflash: Block Diffusion for Flash Speculative Decoding Gains +671 Stars Today at 3.6K TotalDFlash introduces a block diffusion approach to speculative decoding that generates multiple candidate tokens in parallel for faster LLM inference. Based on the paper 'DFlash: Block Diffusion for Flash Speculative Decoding' (arXiv:2602.06036), the repo provides pre-trained draft models for Qwen, Gemma, and other popular LLMs. At +671 stars today, it's the fastest-rising inference optimization tool on GitHub this week.
2026-05-02 / reddit-researchergemma-4-31B-it-DFlash Released: Community Distilled Flash Variant of Gemma 4 — 118 Upvotesz-lab released gemma-4-31B-it-DFlash on HuggingFace, a distilled flash variant of Google's Gemma 4 31B model optimized for faster inference. The release is pending a merged PR before community testing can begin. This follows yesterday's reports of Gemma 4 31B outperforming Qwen 3.6 27B in gamedev benchmarks. 118 upvotes and 29 comments on r/LocalLLaMA indicate strong community interest in efficient Gemma variants.
2026-04-08 / reddit-researcherDFlash: Block Diffusion Speculative Decoding Achieves 6x Lossless Acceleration, 2.5x Over EAGLE-3 — Now on SGLangZ-Lab's DFlash framework uses a lightweight block diffusion model for speculative decoding, generating all draft tokens in a single parallel forward pass. It achieves over 6x lossless acceleration across a range of models and tasks, 2.5x higher speedup than state-of-the-art EAGLE-3. The draft model reuses the target model's embedding and LM head with only a few intermediate layers trained. Works under sampling (temperature=1) and thinking mode with ~4.5x acceleration for reasoning models. Now supported on SGLang. Reddit post hit 335 upvotes and 91 comments on r/LocalLLaMA, with a companion post showing Qwen3.5 27B running at ~65 tok/s with DFlash on 2x 3090 (66 upvotes).

Graph relationships

BENCHMARKED AGAINST
xPress
xPress improves upon dFlash drafter with 30% higher acceptance length.
Source finding
COMPETES WITH
EAGLE-3
DFlash achieves 2.5x higher speedup than EAGLE-3.
Source finding
USES
Gemma
DFlash provides pre-trained draft models for Gemma
Source finding
USES
Qwen
DFlash provides pre-trained draft models for Qwen
Source finding
RELEASED
block diffusion
DFlash introduces block diffusion for Flash speculative decoding
Source finding
BENCHMARKED AGAINST
DominoTree
DominoTree improves upon DFlash's block-diffusion drafting method.
Source finding
BENCHMARKED AGAINST
xPress -> DFlash
xPress improves upon dFlash drafter with 30% higher acceptance length.
Source finding
BENCHMARKED AGAINST
DominoTree -> DFlash
DominoTree improves upon DFlash's block-diffusion drafting method.
Source finding

Source trail

GitHub Muse Glimmer unsloth/Muse-Glimmer-30B-GGUF Meta's technical blog posted on X independent public harness DeepSeek / GitHub The card Tom's Hardware Released April 24 z-lab's approach Modular released

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