Nadav Timor @NadavTimor
LLM inference, speculative decoding, open source. Built novel decoding algorithms – default in Hugging Face Transformers (147k+ ⭐). Making LLMs faster + cheaper github.com/keyboardAnt nyc Joined December 2017-
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Local inference in your vscode is here
Local inference in your vscode is here
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In inference, speculation parallelism sustains high MFU by hiding DP comms bottlenecks
In inference, speculation parallelism sustains high MFU by hiding DP comms bottlenecks
KV cache go brrr with @JunchenJiang's @lmcache! Join us tomorrow to learn more about next‑gen long‑context LLM inference: faster-llms.vercel.app
Nadav Timor retweeted
Algorithms developed by researchers in Prof. @DavidHarel50’s team and Intel Labs enable AI developers around the world to combine the power of different AI models “thinking” as one. The study led by PhD student @NadavTimor was presented at #ICML2025>> bit.ly/faster-ai-eng
Nadav Timor retweeted
Do you want to speed up your inference time by 2X for free? Using speculative decoding (SP) but want to use your own drafter? Want lossless speedup with one line? Go to @NadavTimor talk at ICML tomorrow at 3:30 PM and check out his poster afterward at 4:30 PM. Thank me later!
🎉 #icml2025 oral (top 1%) – our speculative decoding paper: arxiv.org/abs/2502.05202
Nadav Timor retweeted
Twitter! I can't believe no one told me that people are using vision encoders to retrieve document information these days. Based on a tip from @keyboardAnt I read the "ColPali: Efficient Document Retrieval with Vision Language Models" paper, and it is very cool.
Hiding the verification latency of speculative decoding = faster inference. Come chat with me about how! 📍 Poster #245 (Hall 3) 🕒 Today, 15:00–17:30
🚀 Vercel’s #StateOfAI survey highlights: 83% of teams run ≤3 models, 86% don’t train their own, and 46% say the biggest hurdle is latency or cost (23% latency + 23% cost). Devs are leaning hard on third‑party inference—OpenAI alone powers 83% of workloads
🚀 Vercel’s #StateOfAI survey highlights: 83% of teams run ≤3 models, 86% don’t train their own, and 46% say the biggest hurdle is latency or cost (23% latency + 23% cost). Devs are leaning hard on third‑party inference—OpenAI alone powers 83% of workloads
🚀 Calling all OSS contributors to vLLM, SGLang, HF Transformers, TGI, TRT-LLM & more — Join us at the Inference Engines Social @ ICLR 🇸🇬 📍 Singapore EXPO 🗓️ Apr 25, 13:00 Let’s hang out, swap ideas & share what we’re building! 🔗 lu.ma/42pobekx
starting in <45 min - faster-llms.vercel.app
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