DeepSeek open sources DSpark, a new framework to speed up LLM inference by up to 85%

DSpark can make decoding faster, but acceptance quality still determines how much speed the system actually realizes.
techtimes

ISC High Performance 2026 Opens: Post-Moore HPC Faces Open-Source Reckoning

Hamburg's Congress Center (CCH) opens its doors today to the global high-performance computing community as ISC High Performance 2026 begins its five-day run through June 26. Now in its 41st year, ISC ...
GitHub

JetSpec: Parallel Tree Drafting

JetSpec is an implementation of causal parallel tree drafting for fast LLM speculative decoding inference with up to 10x acceptance length, and 1000+ TPS on coding and math tasks using B200 GPUs. A ...
IEEE

A Pragmatic PHY Abstraction Technique for Link Adaptation and MIMO Switching

Abstract: MIMO (multiple input multiple output) techniques are widely employed to improve the performance of wireless systems. These techniques are used to overcome multipath fading and/or improve the ...
GitHub

GitHub - facebookresearch/XLM: PyTorch original implementation of Cross-lingual Language Model Pretraining.

In what follows we explain how you can download and use our pretrained XLM (English-only) BERT model. Then we explain how you can train your own monolingual model, and how you can fine-tune it on the ...
Nature

The molecular receptive range of an odorant receptor

Sensory system description and analysis have traditionally used the notion of a 'receptive field,' which is defined as the stimulus range that can be detected at any particular stage of processing. In ...
note

[For CUDA 16GB] llama.cpp Q4_K_M Batch (prefill→432 tokens/s, decode→74.7 tok/s) / Unsloth Gemma 4 QAT GGUF Support / vLLM v0.22.1

This article has been edited and created by AI. llama.cpp Q4_K_M Batch (prefill→432 tokens/s, decode→74.7 tok/s) / Unsloth Gemma 4 QAT GGUF Support / vLLM v0.22.1 In llama.cpp development, dense ...