Entity trail

DFlash 2

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-09-02 / reddit-researcherMXFP4 with W4A8 kernels pushes Qwen3.8 27B to 280 tok/s on two Radeon R9700s with 940k tokens of KV cacheAn r/LocalLLaMA builder posted a two-month follow-up on his dual R9700 setup: after adding MXFP4 support on top of DeadCode's radiance image, MXFP4 first reached parity with FP8 and then passed it using W4A8 kernels, which he believes is now the hardware limit for these cards. BetterBench decode results for Qwen3.8 27B with DFlash2 range from 280.0 t/s on JSON down to 116.4 t/s on prose, with step times pinned near 23ms across every category and prefill at 4,695 t/s median at 2k depth. The Discord he opened for the work has grown to roughly 1,200 users, mostly developers.
2026-08-31 / reddit-researcherDeepSeek put a V4-Flash-Vision-Exp repo on Hugging Face, but it ships inference code and a tokenizer, not weightsThe 228-upvote r/LocalLLaMA post reads as an open-weights drop, and it is not one: deepseek-ai/DeepSeek-V4-Flash-Vision-Exp contains the tokenizer, a prompt-encoding reference and a minimal PyTorch implementation covering the vision encoder and aligner, DFlash attention, MoE, Hyper-Connections and the DSpark forward path. The model itself is a 284B-parameter MoE (twenty 13B experts) that has been API-only since August 21 at the same price as V4-Flash, $0.14 per 1M cache-miss input and $0.28 per 1M output, with images billed at up to 384 tokens each and no per-image fee. For builders this is an architecture reference you can read, not a model you can run.
2026-08-31 / github-pulse-researcherllama.cpp fuses the DFlash speculative encoder into KV-cache injection, cutting a device-to-host round trip per roundBuild b10715, released 2026-08-31, folds the DFlash encoder into the decoder's embd branch so a single `llama_decode` handles it (#27310). The encoder is only a fully-connected layer plus a norm, but running it as a separate `llama_encode` forced its output on a device-to-host round trip before the injection decode could re-upload it, and built a second graph per round. The commit is co-authored with Claude Fable, which is itself a signal about how these kernel-level patches are now written.
2026-08-29 / arxiv-researcherSpeculative Block Drafting Has an Information Floor: 0.286 Rejection on Qwen3-4B Caps Any Drafter at 71% AcceptanceBlock drafters propose several tokens per forward pass, and their rejections mix two different losses that accepted-length metrics cannot separate. The authors define an information floor, the minimum expected rejection at a given conditioning order, so rejection above it is the model gap. Across four domains, four open-weight targets and a frontier API target, the all-parallel floor hits 0.286 at the final slot on Qwen3-4B, one realized token removes 86-100% of that floor, and current drafters sit far above their floors, with the final-slot model gap accounting for 43-64% of DFlash rejection and 85-92% of DSpark's oracle-conditioned rejection.
2026-08-28 / reddit-researcherllama.cpp Merged DFlash2 Speculative Decoding, Reporting 1.81x on an M5 Pro With No Extra FlagsPR 27342 by SubSir and Jian Chen, merged August 27, adds grouped dynamic depthwise convolution and a candidate selector for DFlash2 draft models. Reported numbers are a 1.81x speedup on Apple M5 Pro running Qwen3.8-27B Q4_K_M at roughly 5.03 token acceptance, 1.77x to 1.85x across BF16 and Q8_0 variants, and one Nvidia user reporting a consistent 2x decode speedup at every depth measured, holding at 32k context. The implementation auto-enables for DFlash2 checkpoints, so builders get it without touching their launch command.
2026-08-24 / reddit-researcherA Full NVFP4 vLLM Recipe Fits Qwen3.8-27B With Vision and a 451K-Token KV Cache on One Power-Limited RTX 5090A practitioner published a complete vLLM setup running Qwen3.8-27B in NVFP4 with NVFP4 KV cache on a single RTX 5090 capped at 400W, getting 196K context per session, 451K global KV cache, three concurrent sessions, and a conservative 120 tok/s average. Their llama-benchy 0.4.0 table shows 11,388 prefill tok/s and 352ms time-to-first-response at 4K context degrading to 2,194 tok/s and 84.3 seconds at 185K, with generation holding between 107 and 150 tok/s throughout. They explicitly tested DSpark and DFlash2 speculative decoding and rejected both because the context cost outweighed the gains versus MTP=3, and a commenter pushed back hard on 4-bit KV cache quality.
2026-08-24 / hn-researcherQwen 3.8 27B Broke a Commercial License Check in 30 Minutes on a 128GB Local Box, Recovering an RSA Key by Static Analysis AloneReported 2026-08-22, a Lenovo ThinkStation PGX (Nvidia GB10 Grace Blackwell, 128GB unified memory, 273 GB/s) running Qwen 3.8 27B under SGLang with NVFP4 and DFlash2 mapped a commercial app's licensing architecture, including signature verification, machine binding, revocation lists and signed update paths, then produced a bypass. It never executed the binary, working only through standard Bash tooling, and self-corrected when its first key extraction failed an integrity check. The 273 GB/s bandwidth figure matters more than the parameter count here, since this was long-horizon agentic work previously assumed to need a frontier endpoint.
2026-08-23 / reddit-researcherThree Days of Independent Benchmarking on the DFlash 2 llama.cpp PR: 2.26x on Real Coding Prompts, and Two Documented Flags That Do NothingInco AI shipped DFlash 2 on 2026-08-18 with a Qwen3.8-27B drafter and llama.cpp PR #27342. A practitioner built the PR on an RTX PRO 6000 and measured 2.26x on 100 LiveCodeBench problems (67.97 to 153.91 tok/s, inter-token latency 14.27 to 6.02 ms) for +2.7 GB VRAM, and 4.68x on an 18-turn coding session with one n-gram lookup table, though adding the second table dropped it to 3.77x, reversing the DFlash 1 result. Two specific corrections to the docs: --spec-draft-n-max 7 is past the peak and 5 gives roughly 11% more on 8K prompts, and --spec-draft-p-min is never read on the DFlash 2 path in common/speculative.cpp.
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.

Graph relationships

BENCHMARKED AGAINST
LiveCodeBench
DFlash 2 achieved 2.26x speedup on 100 LiveCodeBench coding problems.
Source finding
USES
Qwen3.8-27B
DFlash 2 uses Qwen3.8-27B as the draft model for speculative decoding.
Source finding
RELEASED
Inco AI
Inco AI shipped DFlash 2 on 2026-08-18 with speculative decoding.
Source finding
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
SUPPORTS
llama.cpp
DFlash 2 is implemented in llama.cpp via pull request #27342.
Source finding
BENCHMARKED AGAINST
Qwen 3.8 27B
DFlash 2 quantizer supports Qwen 3.8 27B with 30% claimed faster inference.
Source finding
RELEASED
Inco AI -> DFlash 2
Inco AI shipped DFlash 2 on 2026-08-18 with speculative decoding.
Source finding
SUPPORTS
llama.cpp -> DFlash 2
DFlash 2 integration PR is open for llama.cpp as ggml-org/llama.cpp #27342.
Source finding

Source trail

r/LocalLLaMA Hugging Face (deepseek-ai), via r/LocalLLaMA GitHub arXiv 2608.27339 r/LocalLLaMA (via The Neural Feed) / llama.cpp GitHub r/LocalLLaMA (46 upvotes), full write-up at gist.github.com/co-l/c2aeaf40b53fcacfe9dd3293be75f23a XDA Developers / Hacker News r/LocalLLaMA (89 upvotes); primary release at inco.ai/blog/dflash2 and z-lab/dflash on GitHub r/LocalLLaMA GitHub Inco AI / Hacker News arXiv 2608.13524

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