Entity trail

DFlash2

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

Briefing refs

Findings

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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.
2026-08-28 / 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.
2026-08-21 / 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 / 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
Qwen3.8-27B
Achieved 1.81x speedup on M5 Pro with DFlash2
Source finding
SUPPORTS
llama.cpp
PR 27342 merged DFlash2 speculative decoding support
Source finding
USES
Qwen3.8-27B
BetterBench decode results use DFlash2 for inference optimization.
Source finding
BENCHMARKED AGAINST
Qwen3.8-27B -> DFlash2
Achieved 1.81x speedup on M5 Pro with DFlash2
Source finding
SUPPORTS
llama.cpp -> DFlash2
PR 27342 merged DFlash2 speculative decoding support
Source finding
USES
Qwen3.8-27B -> DFlash2
BetterBench decode results use DFlash2 for inference optimization.
Source finding

Source trail

r/LocalLLaMA GitHub r/LocalLLaMA GitHub r/LocalLLaMA (46 upvotes), full write-up at gist.github.com/co-l/c2aeaf40b53fcacfe9dd3293be75f23a XDA Developers / Hacker News

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