Can Phi 3 Medium 14B run on RTX A5000 24GB?
YES — Runs Great
Phi 3 Medium 14B needs ~15.2 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~63 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
67.7 tok/s
TTFT
2861 ms
Safe context
62K
Memory
15.2 GB / 24.0 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 | B | Runs well | 63.0 tok/s | 1678 ms | 62K |
| Coding | B | Runs well | 63.0 tok/s | 3075 ms | 62K |
| Agentic Coding | B | Runs well | 63.0 tok/s | 4473 ms | 62K |
| Reasoning | B | Runs well | 63.0 tok/s | 3635 ms | 62K |
| RAG | B | Runs well | 63.0 tok/s | 5592 ms | 62K |
Quantization options
How Phi 3 Medium 14B (14B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B57 |
Q3_K_S | 3 | 6.9 GB | Low | B58 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Phi 3 Medium 14B on your machine.
Run
ollama run phi3:medium