Inference
CUDA
NVIDIA's parallel computing platform that lets software run computations on NVIDIA GPUs.
In depth
NVIDIA's parallel computing platform that lets software run computations on NVIDIA GPUs. CUDA is the foundation of GPU-accelerated AI inference and training. When you run a local model with Ollama or LM Studio and it uses your NVIDIA GPU, CUDA is doing the heavy lifting. AMD GPUs use ROCm as an alternative, and Apple Silicon uses Metal.
Example
In practice, developers reach for CUDA when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
Common questions
What is CUDA?
NVIDIA's parallel computing platform that lets software run computations on NVIDIA GPUs.
Why does CUDA matter for AI developers?
CUDA sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about CUDA?
Developers Digest publishes tutorials and videos that cover Inference topics including CUDA. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Continued Pretraining
Running more next-token-prediction training on an already pretrained model using a large unlabeled corpus from a target domain, such as code, math, medicine, or a new language.
Inference
Cross-Entropy Loss
The standard loss function for classification and language model training, measuring how much probability the model gave to the correct answer.
Inference
GGUF
A binary file format for storing quantized language models, designed for efficient local inference with llama.cpp and tools built on it.
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