DFLASH2
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Headlines · 8
- Community tester: DFlash2 for Qwen3.8-Flash-Next gives +17% decode speed in llama.cpp v0.8.0
A Reddit tester in r/LocalLLaMA reports that llama.cpp v0.8.0 is the first release supporting DFlash2 drafters for Qwen3.8-Flash-Next, after quantizing their own GGUFs of the drafter. Testing on an Apple M5 Max with Q4_K_XL showed a geometric mean of 87.9 tps for DFlash2 versus 75.1 tps for MTP, a 17% gain, with code prompts at 95.1 tps (+18%) and list prompts at 127 tps (+39%) but prose slightly lower at 51.9 tps (-3%). At around 25K context DFlash2 roughly equalled MTP with some drop-off, and the tester noted its Q4_0 draft head is only about 350MB versus 2.2GB for the ggml MTP head, though the required compute buffer offsets the VRAM savings.
r/LocalLLaMA · 🔥 2 - UkisAI releases two Swift 1.5 reasoning-efficiency models
UkisAI announced Swift 1.5 GLM-5.3-Flash and Swift 1.5 Ornith-9B, with GGUF and other quantized weights available for local inference. The company says GLM-5.3-Flash cut thinking-token usage by 61.4% on MMLU-PRO with a 0.46% average accuracy loss, while Ornith-9B reduced thinking-token usage by 33.5% without an accuracy loss. These results are vendor claims based on UkisAI's own benchmarks and have not been independently verified.
r/LocalLLaMA · 🔥 0 - Six Inference Engines Benchmarked on RTX 5090
A TensorSharp author benchmarked six inference engines running Qwen3.8 27B on one RTX 5090 32 GB, with and without DFlash2 speculative decoding. NInfer led speculative and aggregate concurrent throughput, while llama.cpp recorded the highest plain single-request decode rate; the tests also covered 32K prompts, image input, and a four-turn tool loop. The report attributes results to complete configurations because checkpoints, drafters, and KV formats differed, and its author disclosed being TensorSharp’s creator.
r/LocalLLaMA · 🔥 0 - llama.cpp XMX matrix engine refactor nearly doubles B70 speculative decoding speeds
A Reddit user on r/LocalLLaMA reports that llama.cpp pull request #29864, which refactors Intel XMX matrix engines and is attributed to budiony, made a huge improvement to speculative decoding speeds on an Intel B70 GPU running SYCL. In before/after runs on Muse Glimmer Q6_K (30B) with DFlash2 across nine prompts, throughput rose from 38.0 to 73.0 tok/s on code_python and aggregate wall time fell from 46.86s to 26.68s. The poster notes the acceptance rate dropped from 0.7845 to 0.363 because cheaper token verification allowed n-max to be increased; the numbers are a single, unverified user report.
r/LocalLLaMA · 🔥 0 - KRAFTON AI releases Raon-1 mini, a 30B-A4B open-weight MoE model under Apache 2.0
KRAFTON AI has released Raon-1 mini, a 30B-A4B Mixture-of-Experts language model that the model card says was designed and trained from scratch by the company's AI lab. The model card, surfaced in a r/LocalLLaMA post linking a Hugging Face GGUF preview build, lists an Apache 2.0 license and highlights built-in thinking, instruction following and search, Korean and English ability, and tool calling for agentic workflows with Hermes Agent, Pi and OpenCode. KRAFTON's claims of leading results on math, science, instruction-following and search benchmarks are vendor-supplied and were not independently verified in the supplied material; the card also mentions a DFlash2 draft model for speculative decoding and MXFP8 and NVFP4 quantization, with NVFP4 said to allow inference on a single 32GB RTX 5090.
r/LocalLLaMA · 🔥 0 - Blockway releases Agens Volundr 32B v1 with an MIT-licensed Agens Engine
Blockway posted Agens Volundr 32B v1 on r/LocalLLaMA, saying it addressed issues the community raised against the Preview: repetition loops on SWE-bench tasks fell from 36/50 to 0/50, SWE-bench Verified (50-task subset) rose from 22/50 to 27/50, τ²-bench from 89 to 92/120, and median SWE task time dropped from 124 to 82 minutes. The team also open-sourced Agens Engine under the MIT licence, an inference engine built for the hybrid architecture, claiming 1.4–1.6× faster single-stream decoding than its own SGLang build, plus a roughly 2.85 bits-per-weight build offering 262K context within a 16 GB memory budget. All figures are vendor self-reported: the 16 GB numbers were measured on a 48 GB card under a 16 GiB cap, GPQA drops to 73.7 at that bit-width versus 80.3 at bf16, and the team says stock vLLM cannot load the architecture while SGLang and llama.cpp still require its own builds.
r/LocalLLaMA · 🔥 0 - Tencent releases DSpine parallel drafter for Qwen3-8B speculative decoding
Tencent Hunyuan published tencent/Qwen3-8B-DSpine-b16 on Hugging Face under Apache 2.0, a roughly 1B-parameter parallel drafter for speculative decoding. The model card says each draft position receives the predicted feature of its predecessor at every draft layer, so the causal chain unfolds over network depth while all positions are drafted in parallel. It states inference code was released in October 2026 and the paper appeared on arXiv in September 2026, and reports acceptance-length comparisons on Qwen3-8B in which DSpine exceeds DSpark and DFlash2 at both T=0 and T=1; those figures are Tencent's own.
腾讯混元 · Hugging Face · 🔥 0 - vLLM adds day-0 support for Reflection's 501B Beam model
The vLLM blog says it supports Reflection's Beam from day 0: a 501B-parameter MoE with 23B active parameters per token, optimized on NVIDIA Blackwell, with reasoning, tool calling and structured outputs working out of the box and tensor, data or expert parallelism available. The team says coding agents autonomously optimized the model, making batch-1 decode 2.71× faster and prefill 1.87× faster than its first implementation; with Beam's DFlash2 drafter, a single request reaches 708 tokens/s on TP8 with an acceptance length of 3.7, while adaptive verification adds about 10% throughput at high concurrency. vLLM also worked with SkyRL on day-0 RL support for Beam, with LoRA, BF16 full fine-tuning and MXFP8 RL available directly.
vLLM Blog · 🔥 0
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