Personal update: I am excited to share that I will join @GoogleDeepMind next week after defending my PhD thesis @MITEECS earlier last month. I will be working on generative models that simulate the physical world. Looking forward to the new journey ahead in 2025!
Haotian Tang
109 posts
Joined September 2021
- 🚀 We're thrilled to introduce HART, an efficient AR model that generates stunning 1024x1024 images! 🎨✨ HART delivers: ⚡️ 4.5-7.7x higher throughput 🔋 6.9-13.4x less compute 🔥 top-notch FID & CLIP scores, rivaling diffusion models in quality! Code: tinyurl.com/nkvpnhyk
- Excited to share my #MLSys 2024 best paper 🏆 presentation on AWQ. AWQ democratizes edge LLM deployment 💻 and has been downloaded over 1 million times on Huggingface 🙌!
- Replying to @haotiant1998AWQ website: hanlab.mit.edu/projects/awq Paper: arxiv.org/abs/2306.00978 Code: github.com/mit-han-lab/ll… Joint work with @jilin_14, @jmtang42, @Shang_mit, Wei-Ming Chen, Wei-Chen Wang, @Guangxuan_Xiao, Xingyu Dang, Prof. @gan_chuang, Prof. @songhan_mit.
- Replying to @haotiant1998📄 Paper: arxiv.org/abs/2410.10812 🌐 Project: hanlab.mit.edu/projects/hart 🖥 Demo: hart.mit.edu 💻 Code: github.com/mit-han-lab/ha…
- 🔥Welcome to try out QServe! TRT-LLM efficiency⚡️ + PyTorch flexibility 😄, your LLM serving turn-key solution 🔑🔥🎉Thrilled to introduce QServe, our latest breakthrough in efficient LLM serving with W4-A8-KV4 quantization. 🚀⚡1.2-3.5x higher throughput over TensorRT-LLM. 💵 Matches TensorRT-LLM’s A100 throughput with 3x cheaper L40S GPUs. 👐 Code: github.com/mit-han-lab/qs… (1/4)
- Replying to @haotiant1998✨ How it works: We decompose continuous latents into two parts: 🔹 Discrete tokens for the big picture, modeled by a scalable-resolution AR transformer 🔸 Residual tokens for image details, handled by a lightweight diffusion module (37M parameters, 8 sampling steps)
- What an achievement! Congrats to the team!Our latest update to our Gemini 2.0 Flash Thinking model (available here: goo.gle/4jsCqZC) scores 73.3% on AIME (math) & 74.2% on GPQA Diamond (science) benchmarks. Thanks for all your feedback, this represents super fast progress from our first release just this past
- Replying to @xiuyu_l @GoogleDeepMind and @MITEECSThank you, Xiuyu! See you in the Bay Area!
- Replying to @phillip_lippe @GoogleDeepMind and @m__dehghaniExcited to work together, Phillip!
- Replying to @daoluc_ @GoogleDeepMind and @MITEECSThank you Luc! It’s my great pleasure to work as the TA for 6.5940!
- Replying to @yule_gan @GoogleDeepMind and @MITEECSThank you, Yulu!
- Replying to @vernons @GoogleDeepMind and @MITEECSThank you Vernon!








