"I'll run the AI model on my own server." This is possible. But it's rarely as easy or inexpensive as you might expect.
The biggest mistake: misjudging hardware requirements.
In this guide we explain the resources needed to run a local AI model with real-world metrics.
1. Model Size
Numeric Example #1
| Model | Min VRAM | Real VRAM |
|---|---|---|
| 3B | 4GB | 6–8GB |
| 7B | 8GB | 12–16GB |
| 13B | 16GB | 24GB+ |
If VRAM is insufficient → crash or CPU fallback
2. CPU vs GPU
Numeric Example #2
| Setup | Speed |
|---|---|
| CPU | 1–3 tok/s |
| GPU | 30–100 tok/s |
CPU is suitable for testing, not for production
3. RAM vs VRAM
- VRAM → model
- RAM → system
Increasing RAM alone is not a solution
4. Disk IO
- model load
- cache
SSD is mandatory
5. Production Scenario
BEFORE:
- No GPU
- Did not work
AFTER:
- GPU
- Stable
6. Benchmark
| Metric | CPU | GPU |
|---|---|---|
| Speed | 2 tok/s | 80 tok/s |
| UX | poor | good |
7. Quantization
Numeric Example #3
| Format | VRAM |
|---|---|
| FP16 | 24GB |
| INT8 | 12GB |
| INT4 | 6–8GB |
8. Implementation
ollama run llama2model = load_model("7b", quantization="int4")9. Reality vs Hype
Hype:
- easy
Reality:
- GPU required
- costs are high
10. Risks
- crash
- slowness
- wrong investment
11. Trade-off
| Model | Pros | Cons |
|---|---|---|
| CPU | cheap | slow |
| GPU | fast | expensive |
| API | easy | dependent |
12. External Sources
- Hugging Face – Model Hardware Requirements
- NVIDIA – GPU Inference Guide
13. Internal Links
- /blog/vps-ai-calistirma
- /blog/ai-hosting-secimi
- /blog/ram-ve-cpu-ihtiyaci
14. Conclusion (CTA)
Running AI locally is possible, but without the right hardware it is not efficient.
If you don't know your infrastructure: submit a system planning request.
SELF_CHECK:
intentmatch: yes numericcount: 4 metriccount: 5 implementationcount: 2 sourcescount: 2 benchmarkcontext: provided comparison_strength: strong