Which compute instance for AI models training and inference?
Choosing the right GPU instance is crucial to the success of your AI projects. An optimal configuration not only improves performance, but also keeps costs under control. This guide will help you navigate through the various options available to find the ideal solution for your needs.
Why is the compute instance choice so important?
Your choice of GPU infrastructure has a direct impact on..:
Performance: model training speed and inference latency
Cost: optimizing your budget by avoiding over-sizing
Scalability: ability to scale according to your needs
Reliability: stability of your workloads in production
Which compute instance to choose for model training?
1. Large Language Models (LLMs) models training
Fine-tuning Large Language Models represents one of the most resource-intensive tasks in modern AI development.
The hardware requirements vary significantly based on model size, from smaller 7B parameter models to massive 70B+ architectures. This section will help you select the optimal configuration for your fine-tuning project, ensuring efficient resource utilization while maintaining performance.
Small (less than 7B parameters)
VM
From 22 GB (for 1B parameters models) to 140 GB (to fine-tune models such as DeepSeek-R1 7B)
1 to 3B models: 👉 The most cost-effective: 1xA100 80G
👉 The most efficient:
1xH100 80G 7B models: 👉 1xH200 141 GB
Medium (12B-32B)
VM or Bare-Metal
From 200 to 500 GB (to fine-tune models such as DeepSeek-R1 32B)
12 to 14B models: 👉 2xH200 141 GB 27 to 32B models:
2. Computer Vision models training
Whether you're developing object detection systems, processing medical imagery, or creating next-generation AI art, selecting the right GPU infrastructure is crucial for your success.
The computational requirements for vision tasks vary significantly based on complexity and scale. Classification tasks might require modest GPU power, while advanced generative models demand substantial computational resources. This section outlines three primary categories of computer vision workloads - classification, segmentation, and generation - each with its unique hardware requirements and optimal configurations.
Classification Models
Segmentation Models
Generative Models
3. Audio/Speech models training
Whether you're developing voice recognition systems, building text-to-speech applications, or exploring the cutting edge of AI music generation, selecting the right GPU infrastructure is crucial for successful model training.
This section outlines three primary categories of audio ML workloads - speech recognition, text-to-speech synthesis, and audio generation - each requiring specific hardware configurations to achieve optimal performance.
Speech Recognition
Audio files (.wav, .mp3), Labeled transcripts
100GB-1TB
Whisper, DeepSpeech, Wav2Vec
⭐⭐⭐
Text-to-Speech
Text corpus, Audio pairs
50-500GB
Tacotron, FastSpeech, VALL-E
⭐⭐⭐⭐
Audio Generation
Audio samples, MIDI files
1-2TB
MusicLM, AudioLDM, Stable Audio
⭐⭐⭐⭐⭐
Speech Recognition Models
Text-to-Speech Models
Which compute instance to choose for model inference?
1. Large Language Models (LLMs) inference
Whether you're serving chatbots, content generation, or text analysis applications, choosing the right infrastructure is crucial for balancing performance, cost, and user experience.
The requirements for LLM inference vary significantly based on several key factors: model size (from 7B to 70B+ parameters), user load (from individual testing to thousands of concurrent users), and latency requirements (from real-time chat applications to batch processing). Each of these factors directly impacts your choice of infrastructure, from single GPU instances to distributed multi-GPU deployments.
LLM Inference Sizing - Small Scale (1-50 concurrent users)
LLM inference Sizing - Medium Scale (51-200 concurrent users)
LLM Inference Sizing - Large Scale (201-1000+ concurrent users)
2. Image Generation Inference Sizing
Deploying image generation models like Stable Diffusion for production introduces unique infrastructure challenges compared to traditional ML workload.
The hardware requirements vary significantly based on three key factors: model complexity (from base models to SDXL with refiners), concurrent user load (affecting batch processing and queue management), and image generation parameters (resolution, steps, and additional features like ControlNet or inpainting). Each of these factors directly impacts your choice of infrastructure and can significantly affect both performance and operational costs.
Small scale (1-50 concurrent users)
Medium scale (51-200 concurrent users)
Large scale (201-1000+ concurrent users)
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