Skip to main content

What is the Impact of Quantisation on Memory Utilisation

 

AspectImpact on Memory Utilization
Large Language Models (LLMs)Reduced Model Size: Quantization reduces the number of bits used to represent each weight and activation. For example, converting from 32-bit floating point (FP32) to 8-bit integer (INT8) reduces memory usage by a factor of 4.

Lower Memory Footprint: This reduction in precision leads to a lower overall memory footprint for storing the model parameters and intermediate activations during inference and training. 

Increased Batch Sizes: With lower memory requirements, it becomes possible to process larger batch sizes within the same memory constraints, improving throughput.
Vector DatabasesCompact Embeddings: Quantization reduces the size of vector embeddings stored in the database. For instance, converting vectors from 32-bit to 8-bit representation decreases storage requirements by up to 75%. 

Efficient Indexing: Smaller vector sizes allow for more efficient indexing and faster retrieval operations due to reduced memory bandwidth and cache usage. 

Scalability: The reduction in memory usage enables the handling of larger datasets and more vectors within the same hardware constraints, improving the scalability of the system.

Benefits of Reduced Memory Utilization

  • Cost Efficiency: Lower memory usage translates to reduced hardware costs, as less RAM and storage are required.
  • Energy Efficiency: Less memory usage often results in lower power consumption, contributing to energy efficiency.
  • Performance Improvements: Reduced memory usage can lead to faster data access and processing times, as more data can fit into the faster levels of the memory hierarchy (e.g., caches).
  • Deployment Flexibility: Models and databases with lower memory footprints can be deployed on a wider range of devices, including edge devices with limited resources.
Labels:

Comments

Post a Comment

Popular posts from this blog

Error: could not find function "read.xlsx" while reading .xlsx file in R

Got this during the execution of following command in R > dat Error: could not find function "read.xlsx" Tried following command > install.packages("xlsx", dependencies = TRUE) Installing package into ‘C:/Users/amajumde/Documents/R/win-library/3.2’ (as ‘lib’ is unspecified) also installing the dependencies ‘rJava’, ‘xlsxjars’ trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/rJava_0.9-8.zip' Content type 'application/zip' length 766972 bytes (748 KB) downloaded 748 KB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsxjars_0.6.1.zip' Content type 'application/zip' length 9485170 bytes (9.0 MB) downloaded 9.0 MB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsx_0.5.7.zip' Content type 'application/zip' length 400968 bytes (391 KB) downloaded 391 KB package ‘rJava’ successfully unpacked and MD5 sums checked package ‘xlsxjars’ successfully unpacked ...
Post a Comment
Read more

What is the benefit of using Quantization in LLM

Quantization is a technique used in LLMs (Large Language Models) to reduce the memory requirements for storing and training the model parameters. It involves reducing the precision of the model weights from 32-bit floating-point numbers (FP32) to lower precision formats, such as 16-bit floating-point numbers (FP16) or 8-bit integers (INT8). Bottomline: You can use Quantization to reduce the memory footprint off the model during the training. The usage of quantization in LLMs offers several benefits: Memory Reduction: By reducing the precision of the model weights, quantization significantly reduces the memory footprint required to store the parameters. This is particularly important for LLMs, which can have billions or even trillions of parameters. Quantization allows these models to fit within the memory constraints of GPUs or other hardware accelerators. Training Efficiency: Quantization can also improve the training efficiency of LLMs. Lower precision formats require fewer computati...
Post a Comment
Read more

What is Tensor Parallelism and relationship between Buffer and GPU

  Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU .   It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...
Post a Comment
Read more