Table of Contents
Overview
The AI GPU landscape in 2025 is more diverse than ever. Whether you're a hobbyist training models at home, a startup building AI products, or an enterprise scaling production workloads, there's a GPU for your needs and budget.
TL;DR - Quick Recommendations
- Hobbyist/Learning: RTX 4090 ($1,599) - Best consumer GPU
- Startup/Production: Cloud A100 ($0.80/hr) - Best value
- Enterprise/LLMs: Cloud H100 ($1.99/hr) - Fastest available
Key Factors to Consider
VRAM (Memory)
Most important factor. LLMs need 24GB+ for 7B models, 80GB+ for 70B+.
Tensor Core Performance
Tensor cores accelerate matrix operations. More = faster training.
Memory Bandwidth
HBM >> GDDR6. Critical for large batch sizes and inference.
Price/Performance
Cloud often beats buying. Consider TCO over 2-3 years.
VRAM is King
For AI training, VRAM matters more than raw compute. A 24GB RTX 4090 can train models that a faster 16GB card simply cannot fit in memory. Always prioritize VRAM.
Consumer GPUs
Consumer GPUs offer excellent value for learning, experimentation, and smaller workloads. The RTX 4090 is particularly impressive for AI work.
RTX 4090
Hobbyists, inference, fine-tuning
- Best consumer GPU
- Great for inference
- Available
- 24GB limits large models
- High power draw
RTX 4080 Super
Budget training, inference
- Great value
- Lower power
- Good availability
- 16GB VRAM limiting
- Slower than 4090
RTX 3090
Budget builds, 24GB VRAM needed
- 24GB VRAM
- Good used prices
- Proven for ML
- Older architecture
- High power
RTX 4070 Ti Super
Entry-level ML, inference
- Efficient
- Good price/perf
- Quiet
- 16GB VRAM
- Less headroom
Datacenter GPUs
Datacenter GPUs are designed for serious AI workloads. They offer more VRAM, faster memory, and better multi-GPU scaling than consumer cards.
NVIDIA H100 SXM
Large LLM training, production inference
- Fastest GPU available
- FP8 support
- Transformer Engine
- Extremely expensive
- Limited availability
NVIDIA A100 80GB
Most training workloads, fine-tuning
- Proven workhorse
- Good availability
- MIG support
- Slower than H100
- No FP8
NVIDIA L40S
Inference, smaller training jobs
- Good balance
- Lower cost than A100
- Ada architecture
- GDDR6 vs HBM
- Less memory bandwidth
NVIDIA A10
Inference workloads, edge deployment
- Affordable
- Good for inference
- Low power
- Limited for training
- 24GB only
AI Training Benchmarks
Real-world performance comparison across common AI workloads (RTX 4090 as baseline = 1x):
| Workload | RTX 4090 | A100 80GB | H100 |
|---|---|---|---|
| Llama 2 7B Training | 1x | 2.5x | 6x |
| Llama 2 70B Training | OOM | 1x | 3x |
| Stable Diffusion XL | 1x | 1.8x | 3.5x |
| BERT Fine-tuning | 1x | 2x | 4x |
| GPT-2 Inference | 1x | 1.5x | 2.5x |
| Whisper Large | 1x | 2x | 3.5x |
Recommendations by Use Case
| Use Case | Budget | Mid-Range | Best |
|---|---|---|---|
LLM Training (7B-13B) Need 24GB+ VRAM. Multi-GPU for larger models. | RTX 4090 (24GB) | A100 40GB | A100 80GB |
LLM Training (30B+) Requires 80GB+ or multi-GPU. H100 significantly faster. | Multi-GPU 4090 | A100 80GB cluster | H100 cluster |
LLM Inference Depends on model size. 4090 great for 7B models. | RTX 4070 Ti | RTX 4090 | L40S / A10 |
Image Generation (SD) Consumer GPUs excellent for this. 12GB+ recommended. | RTX 4070 | RTX 4090 | A100 |
Fine-tuning LoRA works on 16GB. Full fine-tune needs more. | RTX 4080 | RTX 4090 | A100 80GB |
Research/Experiments Flexibility matters. Cloud for burst capacity. | RTX 3090 (used) | RTX 4090 | Cloud A100 |
Cloud vs Buy
Should you buy GPUs or rent from the cloud? Here's the math:
Buy Hardware When:
- You need 24/7 access for 2+ years
- You have space, power, and cooling
- Data privacy is critical
- You want to build equity
- Workloads are predictable
Use Cloud When:
- You need burst capacity
- Workloads are variable
- You want latest GPUs (H100)
- No upfront capital available
- You need global distribution
The Math: RTX 4090 vs Cloud A100
An RTX 4090 costs $1,599. At Griddly's A100 rate of $0.80/hr, that's 2,000 hours of A100 time — which is 2.5x faster than the 4090.
For most users, cloud wins on flexibility and total cost.
Our Top Picks for 2025
RTX 4090 — $1,599
The undisputed king of consumer AI GPUs. 24GB VRAM handles most models, excellent for learning, inference, and fine-tuning. Buy if you want local hardware.
Cloud A100 80GB — $0.80/hr on Griddly
80GB VRAM, proven performance, excellent availability. Best for serious training without the H100 premium. Our top recommendation for most teams.
Cloud H100 — $1.99/hr on Griddly
When you need the absolute fastest training. 3-6x faster than A100 for transformer models. Essential for large LLM training and production inference.