Tag

#inference-optimization

Every item tagged inference-optimization, newest first.

20 items

ARIADNE: Agnostic Routing for Inference-time Adapter DyNamic sElection

Researchers propose ARIADNE, a method for dynamically selecting adapters for PEFT models at inference time without needing access to adapter internals. The approach uses only input embeddings and adapter outputs to make routing decisions. This allows for more flexible and scalable deployment of PEFT models in real-world applications. You can apply this method to improve adapter selection in your own PEFT workflows.

Key takeaways

ARIADNE uses only input embeddings and adapter outputs for routing.
No need for adapter internals like weights or gradients.
Enables scalable PEFT deployment with heterogeneous adapter pools.

aarXiv#parameter-efficient-fine-tuning #adapter-selection #inference-optimization

research23h

Next-Latent Prediction Transformers [R]

Microsoft Research introduces Next-Latent Prediction, a self-supervised learning method that trains transformers to predict their own next latent state, enabling more efficient reasoning and planning. This approach complements next-token prediction and allows for up to 3.3x faster inference via self-speculative decoding. Builders can explore using NextLat to improve transformer performance and efficiency in their applications. The method has the potential to unlock more compact world models for

Key takeaways

NextLat trains transformers to predict their own next latent state.
Enables up to 3.3x faster inference via self-speculative decoding.
Complements next-token prediction for more efficient reasoning and planning.

rr/MachineLearning#transformers #self-supervised-learning #inference-optimization

research1d

What is Speculative Decoding? (trending on paperswithco.de) [R]

Speculative decoding is an inference optimization technique that uses a fast draft model to propose future tokens, verified in parallel by a larger target model. This speeds up token generation by 2-3x. It works by having the draft model generate multiple tokens quickly, which are then verified by the target model, allowing for faster and more efficient processing. You can apply this technique to improve performance in applications that rely heavily on token generation.

Key takeaways

Speeds up token generation by 2-3x.
Uses a fast draft model and a larger target model.
Improves performance in token generation applications.

rr/MachineLearning#inference-optimization #token-generation #speculative-decodin

otherMay 11

Building Blocks for Foundation Model Training and Inference on AWS

AWS and Hugging Face have collaborated to provide optimized building blocks for training and deploying foundation models on AWS infrastructure. The integration enables faster and more cost-effective model training and inference. You can leverage these building blocks to streamline your foundation model development workflow. This partnership aims to make foundation model development more accessible and efficient.

Key takeaways

AWS and Hugging Face collaborate on optimized foundation model building blocks.
The integration enables faster and more cost-effective model training and inference.
Streamlines foundation model development workflow on AWS infrastructure.

HHugging Face Blog#cloud-ai #model-training #inference-optimization

modelsJul 21

Accelerate a World of LLMs on Hugging Face with NVIDIA NIM

NVIDIA and Hugging Face have partnered to accelerate LLM deployments on the Hugging Face platform using NVIDIA NIM, a set of inference-optimized containers. This collaboration aims to simplify and speed up the process of deploying multiple LLMs, making it easier for builders to integrate and manage various models. The partnership targets the growing demand for efficient LLM deployment and management. You can now access optimized performance across a range of models.

Key takeaways

NVIDIA NIM brings optimized inference to Hugging Face.
Partnership targets simplified, efficient LLM deployment.
Multiple LLMs can be deployed and managed more easily.

HHugging Face Blog#inference-optimization #multi-llm #partnerships

researchApr 2

Efficient Request Queueing – Optimizing LLM Performance

The study evaluates request queueing strategies for optimizing LLM inference performance. A simple First-In-First-Out (FIFO) queueing approach outperforms more complex methods like priority queueing and batching. FIFO reduced latency by 20-30% compared to other strategies. You can apply these findings to improve LLM deployment efficiency.

Key takeaways

FIFO queueing outperforms priority queueing and batching for LLM inference.
FIFO reduces latency by 20-30% compared to other strategies.
Simple queueing strategies can significantly improve LLM deployment efficiency.

HHugging Face Blog#llm-performance #inference-optimization #queueing

modelsMar 28

🚀 Accelerating LLM Inference with TGI on Intel Gaudi

Hugging Face and Intel collaborated to optimize Text Generation Inference (TGI) on Intel Gaudi hardware, resulting in faster LLM inference. The optimized TGI backend is now available for use. This acceleration enables builders to deploy LLMs more efficiently on Intel Gaudi. The performance gains make it feasible to run LLMs at scale.

Key takeaways

TGI optimized for Intel Gaudi hardware
Faster LLM inference on Intel Gaudi
Enables efficient large-scale LLM deployment

HHugging Face Blog#inference-optimization #hardware-acceleration #intel-gaudi

modelsMay 22

Deploy models on AWS Inferentia2 from Hugging Face

Hugging Face now supports deploying models on AWS Inferentia2, a custom chip designed for high-performance, low-cost inference. This integration allows you to deploy models with optimized performance and cost efficiency. Builders can use Inferentia2 to run models at scale while reducing infrastructure costs. The partnership aims to make AI deployment more accessible and affordable.

Key takeaways

Hugging Face supports AWS Inferentia2 for model deployment.
Inferentia2 offers high-performance, low-cost inference.
Partnership aims to make AI deployment more accessible.

HHugging Face Blog#model-deployment #inference-optimization #cloud-ai

modelsApr 3

Blazing Fast SetFit Inference with 🤗 Optimum Intel on Xeon

Hugging Face and Intel collaborated on optimizing SetFit inference for Intel Xeon processors using Hugging Face's Optimum library. The result is a 2.2x speedup in SetFit inference performance. You can integrate this optimized solution into your applications for faster and more efficient processing.

Key takeaways

2.2x speedup in SetFit inference on Intel Xeon processors.
Optimized using Hugging Face's Optimum library.
Solution available for integration into applications.

HHugging Face Blog#setfit #optimum #intel #inference-optimization

modelsJan 15

Accelerating SD Turbo and SDXL Turbo Inference with ONNX Runtime and Olive

Hugging Face and Microsoft collaborated to optimize SD Turbo and SDXL Turbo inference using ONNX Runtime and Olive. This integration reduces latency by up to 30% and improves throughput. You can deploy these optimized models on Hugging Face's Inference API or use them locally. The optimization enables faster and more efficient image generation.

Key takeaways

Up to 30% latency reduction with ONNX Runtime and Olive.
Optimized models deployable via Hugging Face's Inference API or locally.
Faster image generation for applications.

HHugging Face Blog#inference-optimization #onnx #image-generation

modelsDec 5

Goodbye cold boot - how we made LoRA Inference 300% faster

Hugging Face improved LoRA inference speed by 300% through dynamic adapter loading, eliminating cold boot times. This optimization enables faster model switching and reduces latency for builders using LoRA adapters. The technique allows for more efficient use of resources, making it easier to deploy and manage multiple models.

Key takeaways

300% faster LoRA inference via dynamic loading.
Eliminates cold boot times for faster model switching.
Improves resource efficiency for multi-model deployment.

HHugging Face Blog#inference-optimization #lora-adapters #model-serving

toolsDec 5

Optimum-NVIDIA Unlocking blazingly fast LLM inference in just 1 line of code

Optimum-NVIDIA enables one-line deployment of optimized LLM inference on NVIDIA hardware. This integration streamlines deployment for builders targeting high-performance, low-latency applications. Optimum-NVIDIA abstracts away low-level optimization details, allowing developers to focus on model development. You can now deploy optimized models with minimal code changes.

Key takeaways

One-line deployment of optimized LLM inference on NVIDIA hardware.
Simplifies deployment for high-performance applications.
Abstracts low-level optimization details for developers.

HHugging Face Blog#inference-optimization #nvidia #deployment

modelsNov 7

Make your llama generation time fly with AWS Inferentia2

AWS Inferentia2 chips provide up to 40% faster inference for Llama models compared to Inferentia1. Hugging Face optimized their Transformers library to leverage Inferentia2's performance. You can deploy Llama models on AWS to take advantage of the speedup. The optimization work enables faster and more cost-effective Llama model serving.

Key takeaways

Inferentia2 offers up to 40% faster Llama inference.
Hugging Face optimized Transformers for Inferentia2.
Faster inference reduces serving costs for Llama models.

HHugging Face Blog#inference-optimization #llm-serving #cloud

modelsOct 3

🧨 Accelerating Stable Diffusion XL Inference with JAX on Cloud TPU v5e

Hugging Face and Google collaborated to optimize Stable Diffusion XL inference using JAX on Cloud TPU v5e. The work resulted in a 30% increase in inference speed and 16% reduction in memory usage. You can deploy optimized models on Hugging Face's Inference API or run them locally with Transformers. This optimization enables faster and more efficient image generation.

Key takeaways

30% faster inference speed on Cloud TPU v5e.
16% reduction in memory usage.
Optimized models deployable via Hugging Face's Inference API or local Transformers.

HHugging Face Blog#stable-diffusion #jax #cloud-tpu #inference-optimization

modelsMay 31

Introducing the Hugging Face LLM Inference Container for Amazon SageMaker

Hugging Face and AWS have collaborated on an LLM inference container for Amazon SageMaker, streamlining deployment of Hugging Face models on SageMaker. This integration allows for one-click deployment of Hugging Face models, enabling faster and more efficient model serving. You can deploy models with optimized performance and reduced latency. The container supports popular Hugging Face Transformers and is available for use on SageMaker.

Key takeaways

One-click deployment of Hugging Face models on SageMaker.
Optimized performance and reduced latency for model serving.
Supports popular Hugging Face Transformers.

HHugging Face Blog#model-deployment #cloud-ai #inference-optimization

modelsOct 12

Optimization story: Bloom inference

Hugging Face optimized BLOOM-176B inference to run 30% faster and cost 1.2x less on AWS. The optimization work focused on quantization, knowledge distillation, and model pruning. You can now deploy BLOOM-176B at a lower cost on cloud infrastructure.

Key takeaways

BLOOM-176B inference is 30% faster.
BLOOM-176B costs 1.2x less on AWS.
Optimization techniques included quantization and model pruning.

HHugging Face Blog#inference-optimization #model-optimization #cloud-deployment

toolsMay 10

Accelerated Inference with Optimum and Transformers Pipelines

Hugging Face introduced Optimum, a library for accelerated inference with Transformers. Optimum provides optimized implementations of popular models like BERT and RoBERTa. You can use Optimum to deploy models more efficiently. Optimum supports various hardware platforms.

Key takeaways

Optimum library accelerates Transformers inference.
Optimized for BERT, RoBERTa, and other popular models.
Supports multiple hardware platforms.

HHugging Face Blog#transformers #inference-optimization #hardware-acceleration

modelsMar 16

Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia

Hugging Face and AWS collaborated to optimize BERT inference on AWS Inferentia chips, enabling faster and more cost-effective deployments. The solution leverages Hugging Face Transformers and SageMaker, reducing inference latency and increasing throughput. You can deploy optimized BERT models using Hugging Face and AWS services. This integration helps you accelerate NLP workloads.

Key takeaways

Optimized BERT inference on AWS Inferentia reduces latency and cost.
Hugging Face Transformers integrates with SageMaker for deployment.
Faster NLP workloads enabled for builders.

HHugging Face Blog#transformers #aws #inference-optimization #nlp

modelsJan 13

Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs

A case study on using Hugging Face Infinity with modern CPUs shows that it is possible to achieve millisecond latency for inference. The setup leverages optimized software and hardware configurations. Builders can use these findings to inform their own deployment strategies for low-latency AI applications. This approach may enable cost-effective, high-performance solutions.

Key takeaways

Hugging Face Infinity enables millisecond latency on modern CPUs.
Optimized software and hardware configurations are key.
Low-latency AI deployment strategies can be cost-effective.

HHugging Face Blog#inference-optimization #low-latency #cpu-optimization

modelsJan 18

How we sped up transformer inference 100x for 🤗 API customers

Hugging Face accelerated transformer inference for API customers, achieving a 100x speedup. This was done through a combination of software and hardware optimizations. The improvements enable faster and more cost-effective model serving. You can now deploy models with significantly reduced latency.

Key takeaways

100x speedup on transformer inference.
Achieved through software and hardware optimizations.
Enables faster and more cost-effective model serving.

HHugging Face Blog#transformers #inference-optimization #api