The Dilemma: Local or Cloud?
Mistral 7B is one of the best small LLMs available - it punches well above its weight class. And unlike massive models, it can actually run on consumer hardware.
But should you buy a local GPU or use cloud GPUs? Let's break down the math.
Hardware Requirements
Mistral 7B needs ~14GB VRAM for full precision, or ~7GB with 4-bit quantization:
| Precision | VRAM Needed | Min GPU |
|---|---|---|
| FP16 (full) | ~14 GB | RTX 4090, RTX 3090 |
| 8-bit (INT8) | ~8 GB | RTX 4070, RTX 3080 |
| 4-bit (GPTQ/GGUF) | ~5 GB | RTX 3060, RTX 4060 |
Performance Comparison
| Setup | Tokens/Second | Response Time (100 tokens) |
|---|---|---|
| RTX 4090 (Local) | ~85 tok/s | ~1.2 sec |
| RTX 3090 (Local) | ~60 tok/s | ~1.7 sec |
| M2 Max (Apple Silicon) | ~40 tok/s | ~2.5 sec |
| L40S (Cloud) | ~95 tok/s | ~1.1 sec |
| H100 (Cloud) | ~140 tok/s | ~0.7 sec |
Cost Analysis: The Math
Local GPU Option
Buying an RTX 4090:
- GPU cost: ~$1,600
- Electricity: ~$0.15/kWh × 450W × hours used
- Depreciation: ~30% per year
Break-even calculation:
If cloud L40S costs $0.90/hour, your RTX 4090 pays for itself after:
$1,600 ÷ $0.90 = 1,778 hours of usage
Cloud GPU Option
On GPUBrazil:
- L40S: $0.90/hour (perfect for Mistral 7B)
- A100: $1.60/hour (for batched inference)
- RTX 4090: $0.55/hour (best value for 7B models)
💡 The Key Question
Will you use more than 1,778 hours per year? That's about 5 hours/day. If yes, local wins. If no, cloud is cheaper.
Decision Framework
🖥️ Choose LOCAL GPU if:
- You'll use it 4+ hours daily, every day
- You need offline/air-gapped operation
- You're running a 24/7 service
- You already have a capable GPU
- Privacy is paramount (no data leaves your machine)
☁️ Choose CLOUD GPU if:
- Usage is sporadic or project-based
- You need to scale up/down quickly
- You want access from anywhere
- You don't want to deal with hardware
- You need bigger models sometimes (70B+)
- You're experimenting and not sure of long-term needs
Hybrid Approach: Best of Both
Many teams use a hybrid approach:
- Local for development: Use your existing GPU for testing and iteration
- Cloud for production: Deploy to cloud GPUs for reliability and scale
- Cloud for bigger models: When you need 70B+ models, rent H100s
Real-World Scenarios
Scenario 1: Solo Developer / Hobbyist
Usage: 10 hours/week for side projects
Recommendation: Cloud ($36/month on RTX 4090)
Buying a $1,600 GPU for 40 hours/month doesn't make sense.
Scenario 2: Startup Building AI Product
Usage: 8 hours/day for development + production
Recommendation: Local for dev + Cloud for production
Buy one RTX 4090 for development, use GPUBrazil for customer-facing API.
Scenario 3: Enterprise / Research Lab
Usage: 24/7 multiple models, need to scale
Recommendation: Cloud (GPUBrazil)
Managing hardware is expensive. Cloud gives flexibility and no maintenance.
Quick Setup: Mistral 7B on Cloud
Get Mistral 7B running in 5 minutes on GPUBrazil:
# SSH into your instance
ssh root@YOUR_IP
# Install vLLM
pip install vllm
# Run Mistral 7B server
python -m vllm.entrypoints.openai.api_server \
--model mistralai/Mistral-7B-Instruct-v0.3 \
--port 8000Done! You now have an OpenAI-compatible API running Mistral 7B.
Try Mistral 7B on Cloud GPUs
No hardware investment. Pay only for what you use. Start in minutes.
Get $5 Free Credit →Conclusion
The local vs cloud debate comes down to usage patterns:
- High, consistent usage (5+ hrs/day) → Local GPU
- Variable, project-based usage → Cloud GPU
- Need to scale or use bigger models → Cloud GPU
For most users, starting with cloud makes sense. You can always buy hardware later once you know your actual usage patterns.
Try GPUBrazil with $5 free credit and see if cloud works for your workflow.