Meta's Muse Glimmer 30B model can be run locally on an RTX 3090 GPU using llama.cpp, DFlash speculative decoding, and Pi, achieving speeds of 46 to 127 tokens per second for agentic coding tasks. The …
A developer's field guide benchmarks DFlash, a block-diffusion speculative decoding method, on 30B-scale models, arguing that tokens per second can mislead. The guide highlights DFlash's architectural…
Meta has unveiled Muse Glimmer, a new 30-billion-parameter AI model family designed to run on laptops, and announced plans to open-weight its flagship Muse Spark 1.2 model within weeks. The move posit…
A configuration using the DFlash speculative decoding drafter with Meta Muse Glimmer 30B on an NVIDIA RTX PRO 4000 Blackwell SFF GPU reached 84.64 tokens per second on a coding task, a 4.50 times spee…
NVIDIA's Nemotron 3.5 Lightning, a 30-billion-parameter hybrid Mixture-of-Experts model that activates only 3 billion parameters per forward pass, is now available on Amazon SageMaker JumpStart, deliv…
Meta has released Muse Glimmer-30B, a dense 30-billion-parameter model designed for on-device agentic AI, diverging from the prevailing Mixture-of-Experts trend. The model activates all parameters per…
Meta released Muse Glimmer, a 30-billion-parameter multimodal agentic model distilled from Muse Spark, under the Apache 2.0 license, designed to run on a single consumer GPU or Mac with no network cal…
Meta Superintelligence Labs released Muse Glimmer, a 30-billion-parameter multimodal open model under Apache 2.0, designed for local agent workloads on consumer hardware. The model scores 76 on SWE-Be…
Researchers introduced DominoTree, a training-free best-first draft tree method for speculative decoding that uses Domino's conditional correction to achieve up to 6.6x speedup over autoregressive dec…
Researchers propose DeLS-Spec, a speculative decoding method that decouples long- and short-context experts to accelerate LLM inference. By adding a lightweight local head to the existing DFlash model…
A developer reports that speculative decoding techniques MTP, EAGLE-3, and DFlash can significantly speed up local inference of large language models in llama-cpp. Testing on an NVidia 3060 RTX 12 GB …
Z-Lab's DFlash technique uses diffusion models to accelerate LLM token generation through speculative decoding, achieving up to 123 tokens per second for code generation in tests with Qwen 3 8B on lla…
DeepSeek-AI released DeepSpec, an open-source codebase for training and evaluating draft models for speculative decoding, supporting three draft model algorithms (DSpark, DFlash, Eagle3) and requiring…
DeepSeek open-sourced DSpark, a speculative decoding framework that accelerates per-user generation for DeepSeek-V4 by 57–85% over the MTP-1 baseline. The framework pairs a parallel draft backbone wit…
DeepSeek released DSpark, a speculative decoding framework now live in its DeepSeek-V4 Flash and Pro production API, delivering 51 to 400 percent throughput gains and up to 80 percent latency reductio…
NVIDIA announced that DFlash speculative decoding boosts inference performance on Blackwell GPUs by up to 15x for gpt-oss-120b and nearly doubles interactivity for Llama 3.1 8B compared to EAGLE-3. Th…
Diffusion Language Models (dLLMs) offer a faster alternative to autoregressive LLMs by generating multiple tokens in parallel, but face consistency challenges. Recent innovations like DFlash, Diffusio…
Researchers have developed adaptive speculative decoding, a method that dynamically selects draft lengths at runtime to optimize token generation efficiency in large language models. The approach addr…
Modal Labs released state-of-the-art DFlash speculators for Qwen 3.5 and Qwen 3.6 models on Hugging Face, achieving 5-20% additional speedups and enabling Qwen 3.5 122B-A10B to run at over 1000 tok/s …
Jian Chen, Yesheng Liang, and Zhijian Liu integrated Z Lab's DFlash block diffusion speculative decoding method into SGLang, collaborating with Modal and LMSYS. The team reports up to 4.31x higher thr…