Optimizing Inference in Large Language Models

Optimizing Inference Speed of Large Language Models for Real ...

This article explores practical techniques for optimizing the inference speed of LLMs, making them more suitable for real-time applications.

Best practices for optimizing large language model inference with ...

This guide describes best practices for optimizing inference and serving of open LLMs with GPUs on GKE using the vLLM and Text Generation Inference (TGI) ...

[2408.03130] Inference Optimizations for Large Language Models

Title:Inference Optimizations for Large Language Models: Effects, Challenges, and Practical Considerations ... Abstract:Large language models are ...

Optimizing Inference in Large Language Models: Strategies and ...

Optimizing Inference in Large Language Models: Strategies and Techniques · Pipeline Parallelism: This technique involves partitioning the model ...

Mastering LLM Techniques: Inference Optimization

The most popular large language models (LLMs) today can reach tens to hundreds of billions of parameters in size and, depending on the use case, ...

LLM inference optimization - Hugging Face

Large language models (LLMs) have pushed text generation applications, such as chat and code completion models, to the next level by producing text that ...

Inference Performance Optimization for Large Language Models on ...

Title:Inference Performance Optimization for Large Language Models on CPUs ... Abstract:Large language models (LLMs) have shown exceptional ...

Optimizing Inference on Large Language Models with NVIDIA ...

Originally published at: https://developer.nvidia.com/blog/optimizing-inference-on-llms-with-tensorrt-llm-now-publicly-available/ Today, ...

Large Language Model — LLM Model Efficient Inference - Medium

Model-level optimization: that is, during model inference, the inference efficiency is optimized by designing an effective model structure (such ...

Optimizing Inference on Large Language Models with NVIDIA ...

This library is for batched inference (server use) you can see by their examples and emphasis on fp8 and fp16, (gpu acceleration), they are not using int8 ...

Primer on Large Language Model (LLM) Inference Optimizations

Algorithm Optimization: Improving the algorithms used for inference, such as using more efficient sampling strategies or optimizing the ...

Inference Optimization Strategies for Large Language Models

Explore inference optimization strategies for LLMs, covering key techniques like pruning, model quantization, and hardware acceleration for improved efficiency.

LLM Inference: From Input Prompts to Human-Like Responses

Large language models are highly capable but computationally intensive, making efficient inference a key challenge. Various techniques can optimize the ...

Large Transformer Model Inference Optimization - Lil'Log

Low parallelizability. Inference generation is executed in an autoregressive fashion, making the decoding process hard to parallel. In this post ...

Inference Performance Optimization for Large Language Models on ...

To mitigate the financial burden and alleviate constraints imposed by hardware resources, optimizing inference performance is necessary. In this ...

Deep Dive: Optimizing LLM inference - YouTube

How large language models work, a visual intro to transformers | Chapter 5, Deep Learning. 3Blue1Brown•3.6M views · 40:54. Go to channel ...

Optimizing AI Inference at Character.AI

At Character.AI, we're building toward AGI. In that future state, large language models (LLMs) will enhance daily life, providing business ...

LLM Inference Performance Engineering: Best Practices - Databricks

In this blog post, the MosaicML engineering team shares best practices for how to capitalize on popular open source large language models (LLMs) for production ...

A Survey on Efficient Inference for Large Language Models

[19] concen- trate on model compression techniques within model-level optimization. Ding et al. [20] center on efficiency research considering ...

Optimizing Large Language Models: Balancing Efficiency and Quality

Optimizing large language models involves a delicate balance between efficiency and quality. By understanding the memory footprint of LLMs and ...