Can Phi-4 Mini Reasoning 4B run on RTX 3000 Ada Laptop 8GB?
YES — Runs Great
Phi-4 Mini Reasoning 4B needs ~5.5 GB VRAM. RTX 3000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~61 tok/s.
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
60.8 tok/s
TTFT
3184 ms
Safe context
43K
Memory
5.5 GB / 8.0 GB
Memory breakdown
See how fast it feels
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 60.8 tok/s | 1737 ms | 43K |
| Coding | S | Runs well | 60.8 tok/s | 3184 ms | 43K |
| Agentic Coding | S | Tight fit | 60.8 tok/s | 4632 ms | 43K |
| Reasoning | S | Runs well | 60.8 tok/s | 3763 ms | 43K |
| RAG | S | Tight fit | 60.8 tok/s | 5789 ms | 43K |
Quantization options
How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on RTX 3000 Ada Laptop 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | S88 |
Q3_K_S | 3 | 1.9 GB | Low | S89 |
NVFP4 | 4 | 2.1 GB | Medium | S89 |
Q4_K_M | 4 | 2.3 GB | Medium | S89 |
Q5_K_M | 5 | 2.7 GB | High | S90 |
Q6_K | 6 | 3.1 GB | High | S91 |
Q8_0Best for your GPU | 8 | 4.1 GB | Very High | S90 |
F16 | 16 | 7.8 GB | Maximum | F0 |
Get started
Copy-paste commands to run Phi-4 Mini Reasoning 4B on your machine.
Run
ollama run phi4-miniYour hardware
