Entity trail

DFlash 2

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

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Findings

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Corpus findings

2026-08-21 / reddit-researcherA Hand-Optimized Qwen3.8-27B Stack Hit 381 Tokens/Sec Single-Request on One RTX 3090, Up From 82 Four Days AgoThe author of a hyper-optimized Qwen3.8-27B inference engine for the RTX 3090 posted a follow-up: 82 tps single-request four days ago, then ~114, then ~138 with DFlash2 drafting and lookup-augmented drafting, and now ~133 tps on real chat prompts with 382 tps when the model reproduces its own context. The stack is fp8 KV cache, int8 lm_head and embed_tokens, fp16 recurrent state, int8 activations, W4A16-requantized DFlash2 block drafting, prefix caching, split-KV verify attention and KVarN for 262k context. The number they care about is 15 of 16 tokens accepted per verify step on document-quoting workloads.
2026-08-20 / github-pulse-researcherQwen3.8-27B Serving Configs Hit ~1,094 tok/s at 64 Concurrent on a Single Power-Limited RTX 3090syv-ai/qwen38-27b-rtx3090 publishes two vLLM configs for one 24 GB consumer card at a 250 W power limit with 150k context: a batch mode measuring ~1,094 tok/s steady-state decode at 64 concurrent (942 end-to-end, rising to ~1,222/1,042 with all layers int8), and a single-user mode reaching 114-122 tok/s single-stream via MTP speculation with four cheap drafts, a calibrated int4 lm_head and split-KV verify attention. The most striking number is 381 tok/s at 25k context when the model reproduces its own context, quoting a document or applying an edit, using DFlash2 with 15 drafted tokens per verify step. It notes speculation wins below roughly 8 concurrent users and plain batching above.
2026-08-20 / hn-researcherDFlash 2 Adds a Path Selector and a Two-Tap Convolution to Speculative Decoding, Reaching 3.4x Throughput on Qwen3.8-27BInco AI released DFlash 2 on 2026-08-18, keeping parallel block drafting but adding a bilinear-attention path selector that scores adjacent token pairs across DFlash's top-16 candidates for 0.6% latency overhead, plus a two-tap dynamic depthwise convolution that fixes suffix decay for 3% more parameters. The result is a 21% gain in mean acceptance length over DFlash, 2.7-3.4x throughput versus autoregressive decoding on Qwen3.8-27B, and 3.1-4.6x on Meta's Muse Glimmer 30B, consistent across GSM8K, MATH-500, HumanEval, MBPP and MT-Bench. Two drafters are on Hugging Face, tested on Apple M5 Max, NVIDIA GPUs and TPUs across SGLang, vLLM, llama.cpp, Ollama and oMLX.
2026-08-20 / sources-researcherDFlash 2 Quantizer Lands Support for Qwen 3.8 27B and Meta Muse Glimmer, With a llama.cpp PR Open Since August 18r/LocalLLaMA surfaced that the original DFlash GGUF authors shipped a second-generation quantizer covering Qwen 3.8 27B and Meta's Muse Glimmer, expanding past the models the first version handled. The integration PR is real and verifiable: ggml-org/llama.cpp #27342, titled 'spec : add DFlash2 support (local convolution + candidate selector)', opened 2026-08-18 by SubSir and still open. Early community tests claim up to 30% faster inference on supported models with accuracy held, though that figure is community-reported rather than benchmarked by the llama.cpp maintainers, so treat it as provisional until the PR merges.
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.
2026-05-10 / 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

SUPPORTS
llama.cpp
DFlash 2 integration PR is open for llama.cpp as ggml-org/llama.cpp #27342.
Source finding
BENCHMARKED AGAINST
Meta Muse Glimmer
DFlash 2 quantizer supports Meta Muse Glimmer 30B.
Source finding
BENCHMARKED AGAINST
Meta Muse Glimmer 30B
DFlash 2 achieves 3.1-4.6x throughput on Meta Muse Glimmer 30B.
Source finding
BENCHMARKED AGAINST
Qwen3.8-27B
DFlash 2 achieves 3.4x throughput on Qwen3.8-27B.
Source finding
RELEASED
Inco AI
Inco AI released DFlash 2 on 2026-08-18.
Source finding
BENCHMARKED AGAINST
Qwen 3.8 27B
DFlash 2 quantizer supports Qwen 3.8 27B with 30% claimed faster inference.
Source finding
SUPPORTS
llama.cpp -> DFlash 2
DFlash 2 integration PR is open for llama.cpp as ggml-org/llama.cpp #27342.
Source finding
RELEASED
Inco AI -> DFlash 2
Inco AI released DFlash 2 on 2026-08-18.
Source finding

Source trail

r/LocalLLaMA GitHub Inco AI / Hacker News r/LocalLLaMA (via The Neural Feed) / llama.cpp GitHub arXiv 2608.13524 Meta AI Research blog Hugging Face r/LocalLLaMA GitHub r/LocalLLaMA r/LocalLLaMA / Z-Lab

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