The hottest Quantization Substack posts right now

And their main takeaways
Category
Top Technology Topics

Stanford CRFM suggests moving to workflows rather than tasks for evaluation!

MLOps Newsletter • 58 implied HN points • 04 Sep 23
🕹 Technology Language Models Quantization AI Tools
  1. Stanford CRFM recommends shifting ML validation from task-centric to workflow-centric for better evaluation
  2. Google introduces Ro-ViT for pre-training vision transformers, improving on object detection tasks
  3. Google AI presents Retrieval-VLP for pre-training vision-language models, emphasizing retrieval to enhance performance

Transformer inference tricks

Artificial Fintelligence • 16 implied HN points • 23 Nov 23
🕹 Technology Optimization Quantization Inference
  1. Implement a KV cache for the decoder to optimize inference speed in transformers.
  2. Consider using speculative decoding with a smaller model to improve decoder inference speed when excess compute capacity is available.
  3. Quantization can be a powerful tool to reduce model size without significant performance tradeoffs, especially with 4-bit precision or more.

How is LLaMa.cpp possible?

Artificial Fintelligence • 4 HN points • 16 Mar 23
🕹 Technology Deep Learning Inference Quantization Performance analysis
  1. Large deep learning models like LLaMa can run locally on a variety of hardware with optimizations and weight quantization.
  2. Memory bandwidth is crucial for deep learning GPUs, with memory being the bottleneck for inference performance.
  3. Quantization can significantly reduce memory requirements for models, making them more manageable to serve, especially on GPUs.
Get a weekly roundup of the best Substack posts, by hacker news affinity: