"If you quantize a model to 4-bit, VRAM usage will be roughly 1/4"—this is a feeling you naturally develop when working with local LLMs. However, when I actually ran FP16, AWQ, and GPTQ on vLLM and ...

The jump to 2-bit LLM quantization isn't just about smaller integers. Explore the hardware-software boundary and why extreme compression requires co-design ...

Run a 35B parameter AI model locally on your iPhone using a Mixture of Experts architecture. The Flash iOS port hits 11 ...

I measured "at which token the output diverges when quantized." int8 matched perfectly up to the 190th token, while int4 took a different path at the 2nd token. And the places where it diverged were s ...

Quantization in neural network inference refers to the process of mapping high-precision parameters and activations to lower-precision representations, typically using integer or even binary values.

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

Pinecone has released VQ-bench, an open-source framework for building and benchmarking vector quantization methods, in a ...

This chapter studies speech quantization and compression techniques such as signal companding, differential pulse code modulation, and adaptive differential pulse code modulation. The chapter ...