AI Tips

NVIDIA NeMo Automodel and Hugging Face Diffusers provide a way to fine-tune video and image models at scale, leveraging the power of NVIDIA GPUs. This can lead to significant improvements in model performance and accuracy, especially for complex tasks like video analysis and image recognition.

python finetune_nemo_automodel.py --model_name nemo_automodel --input_data /path/to/data

NVIDIA NeMo Automodel and Hugging Face Diffusers enable efficient fine-tuning of video and image models at scale. This can help improve model performance and reduce training time.

python finetune_nemo_automodel.py

The Headlamp plugin for Kubeflow simplifies the management of AI/ML workloads on Kubernetes, enabling efficient orchestration of notebook servers, distributed training jobs, hyperparameter tuning, and multi-step ML pipelines.

kubectl apply -f headlamp-plugin.yaml

This approach allows for the fine-tuning of models at scale, leveraging the capabilities of NVIDIA NeMo Automodel and Hugging Face Diffusers, which can significantly speed up the process and improve model performance.

python finetune.py --model automodel --data data_dir

NVIDIA NeMo Automodel and Hugging Face Diffusers allow for efficient fine-tuning of video and image models at scale. This integration enables leveraging the strengths of both frameworks to optimize training workflows.

python finetune.py --model nemo_automodel --dataset my_dataset

At Meta's scale, a few milliseconds of latency degradation can significantly impact ads performance. sched_ext, an upstream open-source kernel scheduler, was used to mitigate latency regressions after a Linux kernel upgrade, demonstrating its effectiveness in high-performance environments.

modprobe sched_ext

Meta Engineering used sched_ext to prevent latency degradation in their ad serving fleet after a Linux kernel upgrade. sched_ext can be beneficial for AI/ML applications where low latency is critical.

modprobe sched_ext

NVIDIA CUDA 13.3 includes a new carryless multiplication instruction, which can be used to accelerate cryptographic algorithms. This feature has been available in x86 CPUs for over fifteen years and can now be utilized in NVIDIA GPUs for improved performance.

nvcc -arch=sm_80 -lcudart your_cuda_code.cu

AMD has released Lemonade 11.0, the latest feature release of their local AI server, which supports AMD Ryzen CPUs, AMD Radeon GPUs, and AMD Ryzen AI NPU acceleration. This can be a valuable tool for AI development on AMD hardware.

lemonade-cli

AMD's software team is preparing for the Advancing AI event with the release of TheRock 7.14, which is a modern build system for AMD GPUs. This can be beneficial for developers looking to leverage AMD's GPU capabilities for AI development.

rocminfo

NVIDIA CUDA 13.3 includes a dedicated hardware instruction for carryless multiplication, which can be utilized to build faster cryptography solutions on NVIDIA GPUs.

# Example CUDA code snippet for carryless multiplication
#include <cuda_fp16.h>
__global__ void carryless_multiplication(float16_t *a, float16_t *b, float16_t *c, int n) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n) {
        c[i] = __mul24(a[i], b[i]);
    }
}

Lemonade 11.0 is AMD's latest feature release for their local AI server, supporting AMD Ryzen CPUs, AMD Radeon GPUs, and AMD Ryzen AI NPU acceleration, which is beneficial for AI development on AMD hardware.

# Example command to install Lemonade
sudo apt-get install lemonade

AMD's Lemonade 11.0 is the latest feature release of their local AI server, designed to support AMD Ryzen CPUs, Radeon GPUs, and Ryzen AI NPU acceleration. This release is beneficial for developers looking to deploy AI models locally, providing text-to-speech and other new features. The inclusion of AMD hardware support makes it a valuable tool for those within the AMD ecosystem.

# Command to install Lemonade AI server (hypothetical)
sudo apt-get install lemonade-ai-server

The NVIDIA Cosmos 3 model can be post-trained to achieve over 90% accuracy in vision reasoning tasks with the help of autonomous coding AI agents, significantly reducing the manual effort required. This approach can streamline the development of high-accuracy vision reasoning models.

# Example: Post-training NVIDIA Cosmos 3 model with agent skills
# This is a placeholder for the actual command that would be used to post-train the model
# using autonomous coding AI agents

The NVIDIA Nemotron Model Reasoning Challenge engaged the Kaggle community to explore techniques that can improve reasoning accuracy. By analyzing the leaderboard and solutions from over 5,000 participants, developers can gain insights into innovative methods to enhance AI reasoning capabilities.

# Example: Implementing a technique from Kagglers
# This is a placeholder for the actual command that would be used to implement the technique
# specific to the challenge

NVIDIA Cosmos 3 can be post-trained in one day using autonomous coding AI agents, which can push vision reasoning models above 90% accuracy with almost no manual effort. This approach can significantly reduce the time and resources required to fine-tune and optimize AI models.

# Example command to post-train NVIDIA Cosmos 3 using agent skills
python post_train_cosmos3.py --agent_skills

The NVIDIA Nemotron Model Reasoning Challenge invited the Kaggle community to explore techniques that can improve reasoning accuracy when adapting vision reasoning models. By learning from the top Kagglers, you can potentially push your AI models' accuracy above 90% with minimal manual effort.

# Example command to train a model using Kagglers' techniques
python train_model.py --kagglers_techniques

NVIDIA's blog post explains how kernel fusion can be used to optimize memory traffic and reduce kernel launch overhead in CUDA. By combining multiple kernels into a single kernel launch, data transfer between the CPU and GPU is minimized, and the overhead of launching multiple kernels is reduced, leading to performance improvements.

# Example CUDA code snippet for kernel fusion
__global__ void fusedKernel(float* a, float* b, float* c, int n) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n) {
        a[i] = b[i] + c[i];
        b[i] = a[i] * c[i];
    }
}

NVIDIA's blog post discusses how host offloading can be used to mitigate memory bottlenecks in JAX-based large language model (LLM) training. This approach allows model weights, gradients, and optimizer states to be offloaded from the GPU to the host, thus freeing up GPU memory and allowing for larger models or batch sizes.

# Example command for host offloading in JAX
# This is a conceptual representation and not a direct command
jax.host_offload_to_cpu()

Kernel fusion is a technique that combines multiple kernels into a single one to optimize memory bandwidth and reduce kernel launch overhead, as explained in NVIDIA's blog post.

# Example of kernel fusion in CUDA
__global__ void fusedKernel(float* a, float* b, float* c, int n) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n) {
        a[i] = b[i] + c[i];
        b[i] = a[i] - c[i];
    }
}

NVIDIA's blog post discusses how host offloading can help mitigate memory bottlenecks in JAX-based large language model training, allowing for more efficient use of GPU resources.

jax.host_offloading.enable() # Enables host offloading in JAX

NVIDIA's blog post explains how kernel fusion can optimize memory traffic and reduce kernel launch overhead, which is crucial for improving the performance of GPU-accelerated applications.

# Example of kernel fusion in CUDA
# This is a conceptual representation and not a direct code snippet
__global__ void fusedKernel(float* a, float* b, float* c) {
    // Combined kernel operations
}

NVIDIA NeMo provides a practical guide to generating synthetic data for financial AI research, addressing the constraints of limited and imbalanced real-world financial news data.

nemo train -t nemo_nlp --data_dir <path_to_data> --config_path <path_to_config>

The updates include improvements to the 🤗 Transformers library, which is widely used in the AI community. These enhancements could lead to faster training times and better model performance.

pip install --upgrade transformers

The patches to the Linux kernel's Direct Rendering Manager (DRM) scheduler show potential for delivering much lower job submission latency when the system is loaded with many runnable graphics jobs.

git apply /path/to/drm-scheduler-patches.patch

A set of patches to the Linux kernel's Direct Rendering Manager (DRM) scheduler has shown potential in delivering much lower job submission latency when the system is loaded with many runnable tasks.

git apply < drm_scheduler_patches.patch

Meta's AI storage blueprint at scale involves a combination of AI filesystem, AI blob storage, and a data fabric to enable reliable and fast access to storage for AI workloads.

# Meta's AI storage blueprint would involve setting up a distributed storage system, which is not directly accessible via command line.

AI adoption can be impeded by security concerns, but NVIDIA's approach ensures that security measures are integrated into the hardware, thus not impacting the performance of AI operations.

# NVIDIA's hardware-rooted AI security would be implemented at the driver or firmware level, not directly accessible via command line.

Meta's approach involves using a combination of local SSDs and high-performance NVMe storage to handle the exponential growth in model capabilities and training dataset sizes.

mkdir -p /path/to/ai/data; mount -t tmpfs tmpfs /path/to/ai/data

This can be used to optimize packet processing and improve the performance of BPF programs in networking.

bpftool prog load <prog> type <type>

GeneBench-Pro is a new benchmark for testing AI performance in genomics and scientific research, using complex, real-world datasets. This can help evaluate and compare the performance of different AI models in these critical fields.

pip install genebench-pro

NVIDIA GQE is designed to optimize GPU-accelerated query engines, addressing common constraints like memory and I/O bandwidth, which can be crucial for high-performance computing tasks.

pip install nvidia-gqe

Linux 7.2 includes Cache Aware Scheduling, which optimizes CPU scheduling to reduce cache misses, potentially improving performance on systems with limited memory bandwidth.

sudo apt-get install linux-7.2

GeneBench-Pro is a new benchmark for testing AI performance in genomics, biology, and scientific research. It uses complex, real-world datasets, making it a valuable tool for evaluating AI models in these fields.

git clone https://github.com/openai/GeneBench-Pro

NVIDIA GQE helps in designing GPU-accelerated query engines, which are often constrained by memory and I/O bandwidth. This can be beneficial for systems that require high-performance querying capabilities.

nvidia-smi

Quantization reduces the precision of the model weights, which can significantly decrease the model size and improve inference speed. This is particularly useful for models with long context windows where moving large model weights efficiently is critical to performance.

nemo-quantize --input-model <model> --output-model <quantized_model>

Linux 7.2-rc1 kernel prepatch includes new AMDGPU HDMI 2.1 FRL, AMD ISP4 & CAS features. This can be beneficial for AI developers using AMD GPUs, as it may provide improved support and performance for their hardware.

sudo apt update && sudo apt upgrade -y linux-generic

Quantization is critical for performance when dealing with growing context windows in large models. NVIDIA Model Optimizer can be used to create checkpoints with quantized weights, improving efficiency.

nvidia_model_optimizer --quantize --model_path model.pt

As context windows grow longer, moving large model weights efficiently becomes critical to performance. NVIDIA Model Optimizer can be used for quantization, which is a common method to address this issue.

# Example command using NVIDIA Model Optimizer for quantization
python -m nvidia.dali.plugin.DALIClassWrapper \
  -b 1 \
  -i nvidia_mod_optimizer \
  -o /path/to/output \
  --config /path/to/config

A new patch series has been posted to improve scalability enhancements to the MD RAID5 software RAID code, resulting in up to a 10-17% performance improvement in certain configurations.

# This is a kernel patch and not a direct command, but you can check the status of your RAID5 array with: mdadm --detail /dev/md0

Linux kernel developer Qi Zheng proposed a new feature called Reserved THP to combine the best of HugeTLB and THP kernel functionality, potentially improving performance and resource management.

# This is a kernel feature and not a direct command, but you can enable THP with: echo 'always' | sudo tee /sys/kernel/mm/transparent_hugepage/enabled

As context windows grow longer, moving large model weights efficiently becomes critical. NVIDIA Model Optimizer addresses this through quantization, a common technique to reduce model size and improve performance.

# Example command using NVIDIA Model Optimizer

A new patch series aims to improve scalability of MD RAID5 software RAID code in Linux, with observed improvements of 10-17% in some configurations.

# Patches not yet implemented, no command available

Linux kernel developer Qi Zheng proposed Reserved THP to combine HugeTLB and Transparent HugePages (THP) functionality. This could potentially improve memory management for AI workloads.

# Feature not yet implemented, no command available

As context windows grow longer, moving large model weights efficiently becomes critical to performance. Quantization reduces the precision of weights, which can significantly decrease the size of the model and improve performance.

nvidia_model_optimizer --quantize

As context windows grow longer, moving large model weights efficiently becomes critical to performance. Quantization reduces the size of model weights, enabling faster and more efficient movement of these weights.

model_optimizer = NVIDIAModelOptimizer(model)
quantized_model = model_optimizer.quantize()

Generative AI workloads are rapidly outgrowing the memory and compute budget of single GPUs. NVIDIA TensorRT with multi-device inference support can help scale AI inference across multiple GPUs.

tensorrt/bin/trtexec --workspace=1<<32 --precision=fp16 --batch=1 --input=input,3,224,224 --output=output,1,1,1 --shapes=input:1x3x224x224 --dla_core=0 --allow_gpu_fallback

Shaders are GPU programs that process visual data. Vulkan Descriptor Heaps streamline resource binding, improving the efficiency of shaders in rendering effects.

vkAllocateDescriptorSets

GitHub Copilot's agentic harness delivers strong results across multiple benchmarks and leading token efficiency, while maintaining flexibility to choose among more than 20 models.

git clone https://github.com/github/copilot-evals && cd copilot-evals

This simplifies the deployment of very large language models (vLLMs) on Hugging Face's infrastructure, reducing the complexity of server management.

huggingface-cli jobs start -m your-model-id

NVIDIA's end-to-end support for Vulkan Descriptor Heaps streamlines resource binding, improving the efficiency of shaders that process visual data, which is beneficial for GPU-intensive tasks.

vkAllocateDescriptorSets

NVIDIA TensorRT's multi-device inference support allows scaling AI inference across multiple GPUs, which is crucial for handling the growing memory and compute requirements of generative AI workloads.

tensorrt_multi_device_inference_setup.sh

As AI systems move from single-turn interactions to coordinated multiagent workflows, low-latency inference becomes increasingly important. NVIDIA's DFlash Speculative Decoding can boost inference performance up to 15x on NVIDIA Blackwell.

# Example command for DFlash Speculative Decoding
# This is a placeholder and actual command depends on specific use case
nvidia-dflash --speculative-decoding

Power can account for 40% of the operating expenses to run an AI factory. Each watt can be spent on overhead, data ingestion, training, or generating inference. NVIDIA suggests optimizing the full stack to maximize energy efficiency.

nvidia-smi -pl

Netflix's research into controllable AI video editing explores techniques for more precise manipulation of video content, which can be beneficial for developers looking to improve video editing AI systems.

# Example command for video editing AI model training
python train_video_editor.py --model_name your_model_name --dataset your_video_dataset

This improvement is beneficial for users with early AMD Radeon Graphics Core Next GPUs, enhancing the GPU recovery process in case of hangs.

sudo apt-get install latest-gpu-drivers

The generic code had been a bottleneck in NVMe and other storage subsystem code of the Linux kernel with CRC64-NVMe. ARM64 NEON intrinsics CRC64 code adapted to work on 32-bit ARM can significantly enhance performance.

sudo apt-get install linux-image-7.1

ARM64 NEON-accelerated CRC64-NVMe support merged for Linux 7.1 provides around 6x performance improvement in checksumming algorithms, reducing bottlenecks in NVMe and other storage subsystems.

sudo apt-get install linux-image-arm64-neon

The open-source Lemonade AI server now supports MCP Server Integration, making it more powerful for leveraging AMD Ryzen AI NPUs, Radeon GPUs, and x86_64 CPUs. This integration is beneficial for users seeking '100% free and private' AI usage across Windows and Linux.

# Example command to install Lemonade AI server
sudo apt-get install lemonade-ai-server

The Lemonade AI server v10.8 release integrates with MCP Server, enhancing its capabilities for leveraging AMD Ryzen AI NPUs, Radeon GPUs, and x86_64 CPUs for '100% free and private' AI usage across Windows and Linux.

# Example command to install Lemonade AI server
sudo apt-get install lemonade-ai-server

NVIDIA's blog post discusses techniques for optimizing Transformer-based models for low-precision training, which can significantly reduce the computational resources required for training large language and generative AI models.

# Example command to enable low-precision training
mpu =MegatronPUPaddleModelParallelism(
    num_layers_per_stage=24,
    tensor_parallel_degree=8,
    pipeline_parallel_degree=2,
    expert_parallel_degree=32,
    sequence_parallel_degree=1,
    sequence_parallel_split=1,
    zero_optimization=ZeroOptimization(
        stage=2,
        contiguous_grads=True,
        overlap_comm=True,
        reduce_scatter=True,
        sub_group_size=1,
        reduce_bucket_size=1e8,
        max_norm_sync=True,
    ),
    zero_optimization_config=ZeroOptimizationConfig(
        block_size=1e8,
        red

NVIDIA's blog post details how to optimize transformer-based models for low-precision training, which can lead to faster training times and reduced memory usage on GPUs.

python -m torch.distributed.launch --nproc_per_node=8 train.py --precision.fp16

Advanced fusion kernels can significantly boost the training throughput of Mixture-of-Experts models, which are a key component in large-scale AI systems, by optimizing the communication between experts.

moe_model = MixtureOfExpertsModel()
advanced_fusion_kernels.optimize_training_throughput(moe_model)

NVIDIA BioNeMo provides recipes for fine-tuning biological foundation models with LoRA, allowing for efficient and effective updates to these large models in the field of computational biology.

nvidia_bionemo_recipes = NVIDIABioNeMoRecipes()
lora_finetuned_model = nvidia_bionemo_recipes.fine_tune_biological_model(model, data)

NVIDIA's blog post discusses how to boost the training throughput of Mixture-of-Experts (MoE) models by using advanced fusion kernels, which can significantly improve the efficiency of training large-scale AI systems.

python -m moe_train --advanced-fusion-kernels

NVIDIA BioNeMo provides recipes for fine-tuning large foundation models like ESM2 using LoRA, allowing for efficient and effective updates to these models for specific computational biology tasks.

python -m biodemo.lora_finetune --config-file config.yaml

Legacy infrastructure was not designed for AI era requirements. Large-scale model training remains centralized in data centers, while test-time inference is moving to the edge to reduce latency and bandwidth consumption.

# Example command to deploy AI model on edge device
sudo docker run -d --name ai-edge-model my-ai-model:latest

Legacy infrastructure was not designed for AI requirements. Test-time inference is rapidly shifting to the edge to reduce latency and bandwidth consumption. This shift can significantly improve performance for applications requiring real-time responses.

sudo apt-get install edge-device-software

Legacy infrastructure was not designed for AI era requirements. Large-scale model training remains centralized in data centers, but test-time inference is moving to the edge to reduce latency and bandwidth consumption.

# Example command to deploy AI model at the edge
sudo apt-get install -y edge-ai-model
edge-ai-model --deploy

As large-scale model training remains centralized in data centers, test-time inference is rapidly shifting to the edge to reduce latency and bandwidth consumption, which is crucial for real-time AI applications.

# Example command to deploy model inference at the edge
# This is a placeholder and actual command depends on the specific edge device and software stack
edge_deploy_model.sh

Legacy infrastructure was not designed for AI requirements. Large-scale model training remains centralized in data centers, but test-time inference is rapidly shifting to the edge. This can help reduce latency and bandwidth consumption, which is crucial for real-time AI applications.

sudo apt-get install -y edge-ai-optimization-tool

This process enables faster inference by converting a quantized checkpoint into an NVIDIA TensorRT engine, which is crucial for deploying optimized models in production.

tensorrt_script = 'python post_training_quantization/convert_to_tensorrt.py --fp8_checkpoint /path/to/fp8/checkpoint --output /path/to/output/engine'

Converting a quantized checkpoint into an NVIDIA TensorRT engine enables faster inference, improving model performance and reducing latency.

tensorrt_script = 'python -m torch.ao.quantization.fx.prepare_qat_fx ' + '-m model_fp32 ' + '-m model_qat'

When training spans trillions of tokens across thousands of accelerators, every percentage point of step improvement matters. Using JAX and MaxText with NVFP4 on NVIDIA Blackwell can help achieve better throughput, which is crucial for pre-training frontier LLMs.

# Example command using JAX and MaxText
# This is a placeholder command and should be replaced with the actual usage
jax.run(your_model_training_function)

When training spans trillions of tokens across thousands of accelerators, every percentage point of step improvement matters. Using JAX with MaxText and NVFP4 on NVIDIA Blackwell can significantly improve throughput, leading to faster training times for large language models.

jax.run(your_model, your_data, max_text=True, nvfp4=True)

Mozilla Firefox has merged initial support for Vulkan Video, which allows for GPU-accelerated video decoding. This can lead to performance improvements and reduced CPU usage when playing videos in the browser, especially on systems with Vulkan-compatible GPUs.

# Enable Vulkan Video decoding in Firefox
# This may require enabling specific settings or flags in the browser

This feature allows Firefox to leverage the Vulkan API for video decoding, potentially improving performance and efficiency on systems with Vulkan-compatible GPUs.

firefox --enable-features=VulkanVideoDecode

Ubuntu 26.04 LTS is designed to maximize the value extracted from GPU clusters by focusing on energy efficiency, measured in tokens per watt (TpW). This metric helps CEOs and infrastructure teams manage the cost of AI workloads more effectively.

sudo apt-get install ubuntu-26.04-lts

Google's Gemma 4 introduces advancements in quantization-aware training, which can significantly improve the efficiency of AI models on mobile and laptop devices. This is crucial for deploying models where computational resources are limited.

python train.py --quantize

CUDA-Oxide allows developers to write CUDA GPU kernels in Rust, providing a safer alternative to traditional CUDA C/C++ development. This can lead to more robust and maintainable GPU code.

rustc my_cuda_kernel.rs --crate-name cuda_oxide

This means that users with Apple M3 devices will be able to run Linux, although it may not be immediately useful for end-users due to ongoing development and compatibility issues.

sudo apt update && sudo apt upgrade -y && sudo apt install linux-image-7.2

This means that users with Apple M3 devices will be able to run Linux on their hardware, potentially improving the utility of these devices for users who prefer or require Linux.

sudo apt update && sudo apt upgrade -y && sudo apt install linux-image-7.2

NVIDIA Alpamayo helps in post-training autonomous vehicle models in a closed-loop, which is crucial for developing effective AV policies. This tool can be used to fine-tune and validate models before deployment.

nvidia-alpamayo --train-model --input-data <data>

Optimizing compilers may remove unused function parameters, which can affect kernel tracing or BPF subsystems. To address this, consider the implications of such optimizations on tracing functionality and adjust accordingly.

# Example of optimizing kernel tracing
# This is a placeholder command as the actual implementation depends on the specific kernel function and tracing setup.
# Please refer to the source article for detailed instructions.
echo 'Optimize kernel tracing for unused parameters'

NVIDIA Vera CPU sets a new standard for agentic workloads by enabling AI factories to preprocess and analyze large datasets more efficiently, leading to improved AI model training and scaling.

# Example command for running agentic workloads on NVIDIA Vera CPU
# This is a placeholder command and may vary based on actual usage
nvidia_vera_run --task <task_name> --data <data_path>

NVIDIA DOCA introduces a new class of infrastructure for AI, transforming data into intelligence for autonomous AI agents with unprecedented security and efficiency, making it ideal for building AI factories.

# Example command for using NVIDIA DOCA
# This is a placeholder command and may vary based on actual usage
doca_command --option <option_value>

NVIDIA Alpamayo is designed to help developers post-train autonomous vehicle models in a closed-loop system, which is crucial for refining AV policies and ensuring they perform well in real-world scenarios.

# Example command for post-training with NVIDIA Alpamayo
# This is a placeholder command and may vary based on actual usage
alpamayo_post_train --model <model_path> --data <data_path>

The NVIDIA MCG Toolkit automates AI model documentation, which is crucial for regulatory compliance and understanding model complexity.

nvidia-mcg-toolkit --help

As AI models grow in complexity, regulatory scrutiny intensifies. The NVIDIA MCG Toolkit automates AI model documentation, helping teams comply with frameworks like California's AB-2013 and the EU AI Act.

N/A

NVIDIA Dynamo provides fast startup for inference workloads on Kubernetes, addressing the cold-start problem in production inference deployments where demand fluctuates over time.

kubectl apply -f nvidia-dynamo.yaml

AI applications are moving beyond text generation to multimodal systems that can perceive, search, and reason across images, documents, video, and text. Step 3.7 Flash on NVIDIA GPUs enables this.

nvidia-smi

The NVIDIA MCG Toolkit helps automate AI model documentation, which is crucial for regulatory scrutiny under frameworks like California’s AB-2013 and the EU AI Act.

nvidia-mcg --help

NVIDIA Dynamo provides fast startup for inference workloads on Kubernetes, addressing the cold-start problem in production inference deployments where demand fluctuates over time.

N/A

CUDA 13.3 includes the release of CUDA Python 1.0, which provides a unified GPU programming model. This allows developers to write Python code that can run on both CPUs and GPUs, simplifying the development of GPU-accelerated applications.

import cupy as cp

NVIDIA Dynamo Snapshot is designed to address the cold-start problem in inference deployments by reducing the time it takes to spin up inference replicas. This can be particularly beneficial for fluctuating demand scenarios, allowing for more efficient scaling of inference services.

kubectl apply -f <your_inference_service>

CUDA 13.3 introduces CUDA Python 1.0, which provides a familiar Python-based interface for GPU programming, making it easier to develop and deploy GPU-accelerated applications across various domains.

import cupy as cp

NVIDIA Dynamo Snapshot addresses the cold-start problem in production inference deployments by enabling fast startup for inference workloads on Kubernetes, allowing inference replicas to scale elastically and handle demand fluctuations efficiently.

kubectl apply -f <dynamo-snapshot-config>

NVIDIA CUDA 13.3 brings new capabilities and performance optimizations, including tile programming and compiler autotuning, benefiting developers across the CUDA ecosystem.

nvcc -tile my_kernel.cu

NVIDIA CUDA Tile programming enables developers to write highly optimized GPU kernels within existing C++ codebases, improving performance.

#include <cuda_tile.h>
__global__ void my_kernel() {
  // Use tile-based programming constructs
}

NVIDIA CompileIQ tackles the problem of finding the best compiler options for a specific GPU, unlocking performance potential automatically.

nvcc -autotune option

NVIDIA CUDA Tile programming enables the development of highly optimized GPU kernels within existing C++ codebases, improving performance.

nvcc -tile my_kernel.cu

The kernel community has been exploring the use of large language models (LLMs) for reviewing kernel patches, which could potentially improve the efficiency and accuracy of the review process.

# Example command to use LLM for patch review (hypothetical)

This indicates a growing trend in leveraging AI for software development, specifically in the context of kernel development, which can help improve efficiency and reduce the maintenance burden.

git apply < generated-patch-file.patch

This indicates an increasing trend in the use of AI for software development, particularly in the context of kernel development, which can help automate and speed up the process of fixing issues.

git apply < generated_patch_file.patch

NVIDIA's solution provides real-time visibility into GPU usage across Kubernetes clusters, which is essential for optimizing resource allocation and maximizing the value of AI infrastructure.

# Placeholder command, actual implementation depends on NVIDIA's monitoring tools

NVIDIA's method allows for the synthesis of realistic 3D medical images at scale, addressing data scarcity and privacy issues in radiology AI. This can be crucial for training AI models on diverse and representative datasets.

# Placeholder command, actual implementation depends on NVIDIA's tools and frameworks

Canonical's approach to developing web apps with local LLM inference can help reduce costs and latency associated with metered AI APIs. This enables rapid iteration during development without incurring high costs.

llama-serve --model <model_path> --port <port>

Working with metered AI APIs can be costly and slow down development. By using local LLM inference, developers can iterate rapidly and avoid high costs associated with API calls. This approach is particularly beneficial for web app development where rapid iteration is crucial.

# Example Python code for local LLM inference
from transformers import pipeline

# Load the local LLM model
llm = pipeline('llm', model='local-model-name')

# Perform inference
result = llm("Your input text")
print(result)

CONFIG_SCHED_CACHE has been merged into the mainline kernel, which should improve performance by reducing cache misses. This can be particularly beneficial for AI workloads running on CPU-only systems.

# CONFIG_SCHED_CACHE is enabled by default in Linux 7.2
# No specific command needed, just ensure your kernel is updated

Evaluating an AI model and evaluating an AI agent are related but answer fundamentally different questions. A model benchmark tests the capability of a model, whereas an AI agent evaluation focuses on how well the agent performs in a specific environment or task.

Evaluating an AI model tests its capability, while evaluating an AI agent answers different questions. This distinction is crucial for developers to understand when assessing their AI systems.

This blog post details how to fine-tune NVIDIA's Cosmos Predict 2.5 model using LoRA/DoRA for robot video generation tasks. Fine-tuning allows the model to adapt to specific use cases, improving its performance on tasks like video generation for robotics.

model = AutoModelForCausalLM.from_pretrained('nvidia/cosmos-predict-2.5')
tokenizer = AutoTokenizer.from_pretrained('nvidia/cosmos-predict-2.5')

with torch.no_grad():
    inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits

In this blog post, NVIDIA demonstrates how to fine-tune their Cosmos Predict 2.5 model using LoRA/DoRA for generating robot videos. This approach can potentially improve the quality and accuracy of generated videos, which is crucial for applications in robotics and autonomous systems.

python fine_tune.py --model cosmos-predict-2.5 --strategy lora-dora

Linux's second-in-command Greg Kroah-Hartman has been leveraging new AI fuzzing tools for uncovering Linux kernel bugs, highlighting the effectiveness of AI in identifying critical issues in system software.

# Example command to run AI fuzzing tool (hypothetical)

Valve has hired a leading Mesa developer from AMD to join their team, aiming to improve the Linux GPU drivers for a better gaming experience. This move signifies the importance of optimizing GPU drivers for better performance and compatibility on Linux systems.

sudo apt-get install mesa-utils

Valve continues to expand their open-source Linux graphics driver team, securing top talent to enhance the Linux GPU drivers for a better gaming experience, which can also benefit AI developers running GPU-intensive tasks.

sudo apt-get install mesa-utils

This documentation provides guidelines on what qualifies as a security bug and how AI can be used responsibly to identify kernel bugs, which can help developers improve the security and reliability of their systems.

cat /usr/src/linux-7.1/Documentation/admin-guide/ai-bugs.rst

This documentation helps developers understand the criteria for what constitutes a security bug when using AI to find kernel bugs, promoting responsible AI practices.

cat /usr/src/linux-7.1/Documentation/security/responsible-ai-use.rst

Agentic inference has fundamentally changed the runtime dynamics of inference workloads by introducing non-deterministic trajectories. NVIDIA Vera Rubin platform is designed to solve the scale-up problem in agentic AI, enabling efficient inference on large models.

# Example command to run agentic AI inference on NVIDIA Vera Rubin
nvidia-smi -i 0 --gpu=0 --compute-mode=exclusive_process --threads=1 --mig=1g.1g.1g.1g.1g.1g.1g.1g

This support enables AI developers to utilize Arm Mali G1-Pro GPUs with open-source drivers, expanding the range of affordable hardware options for AI development.

git clone https://github.com/panfrost-driver/panfrost && cd panfrost && ./configure && make && sudo make install

This improvement allows for better utilization of older AMD GPUs on Linux, potentially enabling AI developers to run models on more affordable hardware.

sudo apt-get install mesa-driver

This support enables AI development on devices with Arm Mali G1-Pro GPUs, which are typically found in lower-cost or embedded systems.

git clone https://github.com/panfrost-driver/panfrost && cd panfrost && ./configure && make && sudo make install

Parameter Golf event gathered 1,000+ participants to explore AI-assisted research, coding agents, quantization, and novel model design under strict constraints.

# Placeholder for AI-assisted research commands

Invest weeks in fine-tuning models and discover exporting to production is not smooth. Use NVIDIA's techniques to optimize the serving pipeline and reduce friction.

# NVIDIA's model optimization and serving tips
# Placeholder for specific commands

NVIDIA Fleet Intelligence can help maximize the utilization and efficiency of large GPU fleets. It provides insights into GPU usage and performance, enabling better resource allocation and optimization.

nvidia-fleet-intelligence --help

AWS offers various services and tools that can be used to train and deploy foundation models efficiently. These services can help manage the complexity of large-scale model training and inference.

aws s3 sync s3://my-bucket/path/to/model /path/to/local/model

NVIDIA Fleet Intelligence offers tools for monitoring and optimizing large GPU fleets, enabling efficient resource utilization and performance.

nvidia-fleet-intelligence --query --update

Following a presentation at the Linux storage, file-system, memory management and BPF summit, Jens Axboe was motivated to improve Linux I/O overhead compared to the Storage Performance Development Kit (SPDK), aiming for a 60% increase in per-core I/O performance.

# Apply the patches to the Linux kernel
git apply /path/to/axboe-patches

NVIDIA Dynamo introduces multi-turn agentic harness support, allowing assistant turns to interleave reasoning with tool calls, and user turns to return responses. This is beneficial for applications requiring structured interactions and can be implemented on any NVIDIA GPU.

nvidia_dynamo_agentic_exchange

NVIDIA Labs project CUDA-Oxide 0.1 allows Rust to be used for developing CUDA kernels, potentially improving developer productivity and kernel performance.

cargo install cuda-oxide

NCCL Inspector is a tool that can be used to monitor the performance of NCCL in real-time. It can help identify bottlenecks and issues in the communication between GPUs, leading to faster debugging and optimization of distributed deep learning workloads.

ncclInspector --log /path/to/logfile

Distributed deep learning depends on fast, reliable GPU-to-GPU communication using the NVIDIA Collective Communication Library (NCCL). NCCL Inspector and Prometheus provide real-time performance monitoring and faster debugging.

ncclInspector --log <log_file>

Model quantization reduces VRAM usage and improves inference performance on consumer devices such as NVIDIA GeForce RTX GPUs.

mo --input_model <model> --output_model <quantized_model> --data_type <INT8/FP16>

NVIDIA TensorRT is used to speed up neural network execution in Unreal Engine, which can be beneficial for applications requiring real-time inference, such as in gaming or computer graphics.

# Example command to integrate TensorRT with Unreal Engine NNE inference
# Please refer to NVIDIA's documentation for specific implementation details
# This is a placeholder and not a direct command
tensorrt_command

NVIDIA's TensorRT can be used to accelerate neural network inference in Unreal Engine, particularly for RTX runtime. This can significantly boost image quality and performance in computer graphics applications, making it suitable for high-performance graphics tasks.

# Example: Using NVIDIA TensorRT with Unreal Engine NNE
# This is a conceptual representation and not actual code
import tensorrt as trt
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(TRT_LOGGER)
config = builder.create_builder_config()
config.max_workspace_size = 1 << 30
engine = builder.build_engine(network, config)

Google's Gemma 4 uses multi-token prediction to accelerate inference. This approach predicts multiple tokens at once, which can significantly speed up inference times, especially for models that generate sequences of tokens.

# Example: Multi-token prediction in Gemma 4
# This is a conceptual representation and not actual code
multi_token_prediction = True
inference_speedup = perform_inference(model, multi_token_prediction)

NVIDIA TensorRT is used to speed up neural network inference in Unreal Engine, which can be beneficial for applications requiring real-time image processing and graphics rendering.

# Example command to use TensorRT for inference
# This is a placeholder and actual command may vary based on specific use case
./inference_engine --model=model.trt --input=input_data

ROCm 7.2.3 provides minor updates to the open-source AMD GPU compute and AI stack, which can be beneficial for developers working on AI applications that leverage AMD GPUs.

module load rocm/7.2.3

ROCm 7.2.3 offers minor updates to the open-source AMD GPU compute and AI stack, which can be beneficial for developers working with AMD GPUs to enhance their AI applications.

# Example command to install ROCm 7.2.3
sudo apt install rocm-dkms rocm-utils

The GCC 16.1 compiler has been released with new changes and performance gains over GCC 15, which can benefit AI development by improving compilation times and efficiency.

gcc --version

The article mentions that the 7.1 kernel prepatch has more patches than usual, likely due to AI tooling. This implies that AI tooling is becoming more prevalent in kernel development, which could lead to more efficient and effective kernel patches.

N/A

AMD GAIA (Generative AI Is Awesome) is an open-source software that leverages the Lemonade SDK to facilitate the creation of AI agents on Windows and Linux PCs. This can be beneficial for developers looking to build AI applications that run locally without relying on cloud-based services.

git clone https://github.com/AMD-GAIA/gaia && cd gaia && ./build.sh

AMD's GAIA (Generative AI Is Awesome) is an open-source software that leverages the Lemonade SDK to facilitate the creation of AI agents on PCs with local AI processing capabilities. This can be beneficial for developers looking to build AI applications without relying on cloud-based services, enhancing privacy and reducing latency.

git clone https://github.com/AMD-GAIA/GAIA.git && cd GAIA && ./build.sh

NVIDIA TensorRT is used to speed up neural network inference in Unreal Engine, which can be beneficial for applications that require real-time graphics processing. The blog post suggests that using TensorRT can lead to significant performance improvements for tasks such as image quality enhancement and performance optimization.

# Example command to integrate TensorRT with Unreal Engine NNE
# Please refer to NVIDIA's documentation for specific implementation details
# https://developer.nvidia.com/blog/speed-up-unreal-engine-nne-inference-with-nvidia-tensorrt-for-rtx-runtime/

NVIDIA's blog post discusses the use of AI agents to automate the translation of GPU kernels from Python to Julia, showcasing the potential for AI to assist in code portability and expansion across different programming environments.

# Example command to translate cuTile Python to cuTile.jl
# This is a conceptual representation and may not be directly executable
aitool translate --input cuTile.py --output cuTile.jl

cuTile is a tile-based programming model that helps developers write GPU kernels focusing on tile-level operations such as loads, stores, and computations. This can lead to better performance and efficiency when programming for GPUs.

# Example of using cuTile for GPU kernel development
# This is a conceptual representation and not actual code
from cutile import Tile

# Define tile dimensions
tile = Tile(16, 16)

# Perform tile-based operations
# ...

This patch series, originally started by a NVIDIA engineer in early 2025, aims to improve system performance by accelerating page migration. AMD's involvement suggests that this optimization could benefit a wide range of systems, not just those with AMD hardware.

git apply amd_page_migration.patch

This patch series, originally started by a NVIDIA engineer, is now being worked on by AMD to accelerate page migration, which can lead to better performance in Linux systems.

git apply accelerated-page-migration.patch