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TranslatedProvides a practical interactive interface for large language models (LLMs) such as GPT/GLM, with special optimization for the paper reading/polishing/writing experience. Modular design, supports custom shortcut buttons & function plugins, supports project analysis & self-translation for Python, C++ and other projects, PDF/LaTeX paper translation & summarization, supports querying multiple LLM models in parallel, and supports local models such as chatglm3. Integrates Tongyi Qianwen (Qwen), deepseekcoder, iFlytek Spark, ERNIE Bot (Wenxin Yiyan), llama2, rwkv, claude2, moss, and more.

RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.

TranslatedOpen-source manuscript of 《深入理解 AI Infra:量化分析与系统设计》 (In-Depth Understanding of AI Infra: Quantitative Analysis and System Design), by 李博杰 (Li Bojie): starting from hardware constraints and model architecture, it quantitatively derives the system design of LLM inference and training. Includes the full text of the book, a PDF, and companion calculation tools and experiments

  • accelerator
  • ai-infra
  • ai-infrastructure
  • book

Serve large Qwen models fast on the GPUs you actually own. Qwen3.8-27B on a single 24 GB card with vLLM: 127 tok/s single-user (381 when the answer quotes the prompt), ~1,035 tok/s at 64 concurrent, 150k-262k context. vLLM patches, requant pipeline, benchmarks.

Official SGLang x Datawhale course on LLM inference: understand inference, build a mini-sglang from scratch, then read the real SGLang source and land your first PR. Available in English and Chinese.

One keyboard and mouse for a Mac and a Windows PC over the local network, in either direction.

  • clipboard-sync
  • keyboard-sharing
  • macos
  • mouse-sharing