Run AI models on your own hardware - no API costs, but hardware required.
Cost Comparison
| Approach | Hardware Cost | Running Cost | Quality |
|---|
| API (GPT-4o) | $0 | $2.50/1M in | Excellent |
| API (Llama 3.1 70B) | $0 | $0.65/1M in | Great |
| Local (RTX 4090) | $1,600 | Electricity | Good |
| Local (A100) | $15,000 | Electricity | Great |
Hardware Requirements
Consumer GPUs
| GPU | VRAM | Models | $/hour |
|---|
| RTX 4090 | 24GB | Llama 3.1 8B, Mistral 7B | $0.15 |
| RTX 3090 | 24GB | Llama 3.1 8B, Mistral 7B | $0.12 |
Data Center GPUs
| GPU | VRAM | Models | $/hour |
|---|
| A10G | 24GB | Llama 70B (4-bit) | $1.01 |
| A100 | 40GB | Llama 70B | $2.93 |
| A100 | 80GB | Llama 70B (full) | $3.50 |
| Tool | License | Best For |
|---|
| Ollama | MIT | Easy setup, Mac/Linux |
| LM Studio | Proprietary | Windows GUI |
| llama.cpp | MIT | CPU + GPU, quantized |
| vLLM | Apache 2.0 | Production, fast |
Model Quantization
| Format | Size Reduction | Quality Loss |
|---|
| FP16 | 1x (baseline) | None |
| INT8 | 50% | Minimal |
| INT4 | 25% | Small |
Operating Costs
Home User
| Component | Cost |
|---|
| RTX 4090 power | $0.05-0.15/kWh |
| 8 hours/day | $1.20-3.60/month |
Server (A100)
| Component | Cost |
|---|
| A100 (1 hr) | $3.50 |
| 100 hours/month | $350 |
| Electricity | $50 |
| Monthly | $400 |
Comparison: API vs Local
| Factor | API | Local |
|---|
| Upfront cost | $0 | $1,600-20,000 |
| Monthly cost | $50-500 | $15-400 |
| Setup time | 5 min | 1-10 hrs |
| Privacy | Data to vendor | 100% local |
Best For
- Privacy-sensitive data
- High-volume applications
- Offline capabilities
- Model experimentation
- Cost-effective at scale