NVIDIA CUDA 13.1 introduces CUDA Tile, a tile-based programming model, and performance gains across developer tools and libraries. It also features runtime API exposure of green contexts and a rewritten CUDA programming guide.
CUDA Tile is a new Python package that simplifies GPU programming by automatically tiling loops, handling data transfer, and optimizing memory access. It allows developers to write concise and readable code that leverages the full power of NVIDIA GPUs without needing to manually manage the complexities of parallel programming.
This article details the integration of Docker Model Runner with the NVIDIA DGX Spark, enabling faster and simpler local AI model development. It covers setup, usage, and benefits like data privacy, offline availability, and ease of customization.
Canonical announced today that they will formally support the NVIDIA CUDA toolkit and also make it available via the Ubuntu repositories. This aims to simplify CUDA installation and usage on Ubuntu, particularly with the rise of AI development.
A user, nicholasdavidroberts, expresses gratitude to Daniel for providing a PPA and patched 390 driver that resolved their NVIDIA driver compilation issues on Ubuntu 22.04 with kernel 6.5.0-14.
```
execute_with_retries apt-get install -y -qq gcc-12
update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 11
update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 12
update-alternatives --set gcc /usr/bin/gcc-12
```
```
execute_with_retries apt-get install -y -qq gcc-12
update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 11
update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 12
update-alternatives --set gcc /usr/bin/gcc-12
```
Learn how GPU acceleration can significantly speed up JSON processing in Apache Spark, reducing runtime and costs for enterprise data applications.
The article discusses the competition Nvidia faces from Intel and AMD in the GPU market. While these competitors have introduced new accelerators that match or surpass Nvidia's offerings in terms of memory capacity, performance, and price, Nvidia maintains a strong advantage through its CUDA software ecosystem. CUDA has been a significant barrier for developers switching to alternative hardware due to the effort required to port and optimize existing code. However, both Intel and AMD have developed tools to ease this transition, like AMD's HIPIFY and Intel's SYCL. Despite these efforts, the article notes that the majority of developers now write higher-level code using frameworks like PyTorch, which can run on different hardware with varying levels of support and performance. This shift towards higher-level programming languages has reduced the impact of Nvidia's CUDA moat, though challenges still exist in ensuring compatibility and performance across different hardware platforms.
PygmalionAI's large-scale inference engine designed for serving Pygmalion models to a large number of users with blazing fast speeds. Integrates work from projects like vLLM, TensorRT-LLM, xFormers, AutoAWQ, AutoGPTQ, SqueezeLLM, Exllamav2, TabbyAPI, AQLM, KoboldAI, Text Generation WebUI, and Megatron-LM.
Lambda Stack is an all-in-one package that provides a one line installation and managed upgrade path for deep learning and AI software, ensuring that you always have the most up-to-date versions of PyTorch, TensorFlow, CUDA, CuDNN, and NVIDIA Drivers.
This is why cuda-12 doesn't work with podman 3.4.4 on ubuntu 22.04 I think:
- Rootless configuration for nvidia container runtime
- Setup missing hook for nvidia container runtime
- Increase memlock and stack ulimits
- Rootless configuration for nvidia container runtime
- Setup missing hook for nvidia container runtime
- Increase memlock and stack ulimits
