What are the Best GPUs for Running AI models?

Vishnu Subramanian

Founder @JarvisLabs.ai

Match your GPU to your workload: H100/H200 for the largest generative models, A100 for balanced performance, RTX6000 Ada/A6000 for mid-sized workloads, and A5000/RTX5000 for smaller models and experimentation. The right GPU depends on your specific generation task—text, image, audio, or video—not simply price point.

Matching GPUs to AI Workload Types

After building JarvisLabs' infrastructure and working with diverse AI teams, I've learned that choosing the right GPU starts with understanding your specific workload characteristics. Let's break down which GPUs align with different AI tasks:

For Generative AI Workloads

Generative AI has unique hardware demands depending on the modality and model size:

Text Generation (LLMs)

• Large LLMs (>30B parameters): H100/H200 provide the memory capacity needed for models like Llama3 70B, Claude, or GPT equivalents. However, with quantization, A100s can effectively run these models as well.

• Medium LLMs (7-30B parameters): RTX6000 Ada/A6000 (48GB) handle models like Llama3 8B or Mistral 7B comfortably, with enough headroom for decent batch sizes in production.

• Small LLMs (<7B parameters): A5000 (24GB) and RTX5000 (16GB) run smaller LLMs efficiently, supporting many production chatbots and text generation services built on 3-7B parameter models.

Image Generation

• High-resolution or complex models: H100/H200 excel at running SDXL, Midjourney-equivalent models, or when generating multiple images concurrently.

• Standard diffusion models: A100 and RTX6000 Ada/A6000 efficiently handle Stable Diffusion and similar models, offering good throughput for production image generation services.

• Optimized or smaller models: A5000 can run most diffusion models with slight optimizations, making it cost-effective for many image generation workflows.

Audio Generation

• Complex text-to-speech/music generation: Models like AudioLDM or MusicGen benefit from A100 or higher when generating high-quality, longer audio clips.

• Standard voice synthesis: A6000/RTX6000 Ada work well for most audio generation tasks, balancing quality and cost.

• Lightweight TTS systems: A5000 and even RTX5000 support many production voice generation systems, especially with optimized models.

Video Generation

• Text-to-video models: These are among the most demanding generative workloads. H100/H200 provide the best experience for models like Sora-equivalents or high-resolution video generation.

• Short clip generation: A100 can handle shorter video generations with reasonable quality.

• Frame interpolation/lightweight video tasks: A6000/RTX6000 Ada offer sufficient performance for many video enhancement tasks.

For Training New Models

Training requires significant computational power and memory, with requirements varying based on model size:

• Large model training (>30B parameters): H100 and H200 GPUs provide the memory capacity (80GB and 141GB) and bandwidth needed for efficient training of foundational models. Their Transformer Engine architecture specifically accelerates training of transformer-based networks.

• Medium model training (7-30B parameters): A100 (40GB) and RTX6000 Ada/A6000 (48GB) offer sufficient memory and computational power for training medium-sized models. These GPUs balance performance with cost-effectiveness.

• Small model training (<7B parameters): A5000 (24GB) and even RTX5000 (16GB) can effectively train smaller models, especially when using memory optimization techniques. Many teams successfully train production-ready smaller models on these GPUs.

For Fine-tuning Existing Models

Fine-tuning typically requires less compute than full training but similar memory capacity:

• Large model fine-tuning: H100/H200 are ideal for fine-tuning models like Llama3 70B, but A100s can handle this with quantization techniques.

• Medium model fine-tuning: RTX6000 Ada and A6000 excel at fine-tuning models in the 7-30B range, offering good memory capacity (48GB) at a more accessible price point.

• Small model fine-tuning: A5000 and RTX5000 work well for fine-tuning smaller models up to about 13B and 7B parameters respectively, making them cost-effective options for iterative development.

For Inference Deployment

Inference workloads prioritize different GPU characteristics than training:

• High-throughput, latency-sensitive inference: For applications needing maximum tokens per second or minimal latency, H100/H200 offer advantages through their specialized Transformer Engine and memory bandwidth.

• Balanced inference workloads: A100, RTX6000 Ada, and A6000 provide strong inference performance for most production applications. Many teams successfully run production inference on these mid-tier options.

• Cost-optimized inference: A5000 and RTX5000 can serve as effective inference engines for smaller models or when using quantization. With proper optimization, these GPUs support many production inference workloads at significantly lower cost.

Memory Requirements by Model & Generation Type

Memory is often the critical constraint for AI workloads. Here's a general guide for matching VRAM to various generative tasks:

Text Generation (LLMs)

• 7B parameter models: Minimum 16GB VRAM (RTX5000)
• 13B parameter models: Minimum 24GB VRAM (A5000)
• 30B parameter models: Minimum 48GB VRAM (A6000/RTX6000 Ada)
• 70B parameter models: Minimum 80GB VRAM (H100) or 40GB with quantization (A100)

Image Generation

• Standard Stable Diffusion: Minimum 16GB VRAM (RTX5000)
• SDXL or high-resolution: Minimum 24GB VRAM (A5000)
• Multiple concurrent generations: 40GB+ VRAM (A100 or higher)

Audio/Video Generation

• Basic audio generation: Minimum 16GB VRAM (RTX5000)
• High-quality audio: Minimum 24GB VRAM (A5000)
• Short video generation: Minimum 40GB VRAM (A100)
• High-quality video generation: 80GB+ VRAM (H100/H200)

Keep in mind that memory requirements can be reduced through optimization techniques like quantization, which can reduce requirements by 1.5-2×.

Production Workloads Across GPU Tiers

It's important to note that production workloads run successfully across all GPU tiers:

• Large-scale production: While H100/H200 offer maximum performance, many production services run effectively on A100s with optimization.

• Mid-tier production: RTX6000 Ada and A6000 support numerous production inference and fine-tuning workloads, especially for models under 30B parameters.

• Cost-efficient production: For smaller models and optimized pipelines, A5000 and even RTX5000 support various production workloads—particularly when horizontal scaling (multiple GPUs) is employed.

Budget-Conscious Strategies

When building JarvisLabs, we learned several approaches for maximizing GPU value:

• Multiple GPUs vs. single high-end GPU: For some workloads, two A6000s can outperform a single H100 at similar cost.

• Optimize before upgrading: Techniques like quantization, gradient checkpointing, and efficient attention implementations can dramatically reduce memory requirements.

• Time-sharing vs. dedicated resources: Consider whether your workload requires 24/7 dedicated resources or if you can leverage time-sharing to access higher-end GPUs at lower overall cost.

Finding Your Balance

The most important lesson from my experience building GPU infrastructure is this: the best GPU isn't always the most powerful one—it's the one that aligns with your specific workload, optimization capabilities, and budget constraints.

For generative AI specifically, consider the modality (text, image, audio, video), model size, and batch requirements before choosing hardware. Many teams successfully run production text and image generation on mid-tier GPUs with proper optimization.

What specific generative AI tasks are you looking to run? The optimal hardware choice depends on the details of your particular workload.