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#hardware

Every item tagged hardware, newest first.

7 items

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Cheapest way to run GLM 5.x locally that's not a unified memory system?

The discussion explores cost-effective ways to locally run GLM 5.x models, focusing on 4bit quantization. Users share experiences with CPU-only setups like Sapphire Rapids ES 56core + DDR5 and multi-GPU configurations with partial offloading. The conversation aims to identify viable options for running large models like GLM 5.x outside unified memory systems. You can consider various hardware configurations for efficient local deployment.

Key takeaways
  • Sapphire Rapids ES 56core + DDR5 is a potential option for running GLM 5.x locally.
  • Multi-GPU setups with partial offloading are also being explored.
  • The discussion is not limited to GLM 5.x, but also applies to similarly sized models.
rr/LocalLLaMA#local-llm#hardware#quantization
otherOct 10

Arm will be @ PyTorch Conference, Join Us!

Arm will be attending the PyTorch Conference. The company will likely showcase its work on optimizing PyTorch for Arm-based hardware. You can join the conference to learn more about Arm's efforts in the AI and machine learning space. The event will cover various topics related to PyTorch and its applications.

Key takeaways
  • Arm attending PyTorch Conference
  • Optimizing PyTorch for Arm-based hardware
  • Conference covers PyTorch applications
HHugging Face Blog#pytorch#hardware#ai
otherJun 16

Groq on Hugging Face Inference Providers 🔥

Groq has joined Hugging Face as an inference provider, offering optimized performance for large language models. This partnership enables seamless deployment of AI models on Groq's hardware. You can now deploy models on Groq's infrastructure through the Hugging Face platform. Builders can leverage Groq's performance for their AI applications.

Key takeaways
  • Groq joins Hugging Face as an inference provider.
  • Enables deployment of AI models on Groq's hardware via Hugging Face.
  • Partnership offers optimized performance for large language models.
HHugging Face Blog#inference#hugging-face#hardware
otherDec 5

AMD + 🤗: Large Language Models Out-of-the-Box Acceleration with AMD GPU

AMD and Hugging Face have collaborated to enable out-of-the-box acceleration of large language models on AMD GPUs. This integration allows developers to deploy models more efficiently without requiring custom optimization. The partnership aims to make AI deployment more accessible and cost-effective for builders.

Key takeaways
  • AMD GPUs now support out-of-the-box LLMs acceleration via Hugging Face.
  • No custom optimization required for deployment.
  • Partnership targets more accessible and cost-effective AI deployment.
HHugging Face Blog#gpu-acceleration#ai-deployment#hardware
modelsFeb 6

Accelerating PyTorch Transformers with Intel Sapphire Rapids - part 2

Intel and Hugging Face collaborated to optimize PyTorch transformer inference on Intel Sapphire Rapids processors. The work resulted in up to 2x faster inference performance for certain transformer models. You can reproduce the results and apply similar optimizations to your own models using the provided code and benchmarks.

Key takeaways
  • Up to 2x faster inference on Sapphire Rapids processors.
  • Optimizations available for PyTorch transformers.
  • Code and benchmarks provided for reproducibility.
HHugging Face Blog#pytorch#transformers#optimization#hardware
modelsMay 26

Graphcore and Hugging Face Launch New Lineup of IPU-Ready Transformers

Graphcore and Hugging Face have collaborated to launch a new lineup of IPU-ready transformers, optimized for Graphcore's Intelligence Processing Units (IPUs). This partnership aims to make it easier for developers to deploy transformer models on Graphcore's hardware. The optimized models are available on Hugging Face's model hub. You can now access and deploy these models for various AI applications.

Key takeaways
  • Graphcore and Hugging Face partnered on IPU-optimized transformers.
  • The optimized models are available on Hugging Face's model hub.
  • Developers can deploy these models on Graphcore's IPUs for AI applications.
HHugging Face Blog#ipu#transformers#hardware#partnership
toolsSep 14

Introducing Optimum: The Optimization Toolkit for Transformers at Scale

Hugging Face introduced Optimum, a toolkit for optimizing transformer models at scale. Optimum provides a set of tools and techniques for optimizing transformer models, enabling faster and more efficient deployment. You can use Optimum to optimize your transformer models for specific hardware and deployment scenarios. This helps you reduce costs and improve performance.

Key takeaways
  • Optimum is a toolkit for optimizing transformer models at scale.
  • It provides tools and techniques for faster and more efficient deployment.
  • Optimum enables optimization for specific hardware and deployment scenarios.
HHugging Face Blog#transformers#optimization#hardware