Phi 3 Medium 14B needs ~14.4 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~20 tok/s.
Operating mode
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
Tight fit
Decode
19.6 tok/s
TTFT
9859 ms
Safe context
24K
Memory
14.4 GB / 16.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 19.6 tok/s | 5378 ms | 24K |
| Coding | B | Tight fit | 19.6 tok/s | 9859 ms | 24K |
| Agentic Coding | C | Very compromised (needs ~0.7 GB host RAM) | 12.3 tok/s | 22933 ms | 24K |
| Reasoning | B | Tight fit | 19.6 tok/s | 11651 ms | 24K |
| RAG | C | Very compromised (needs ~0.7 GB host RAM) | 12.3 tok/s | 28666 ms | 24K |
How Phi 3 Medium 14B (14B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B60 |
Q3_K_S | 3 | 6.9 GB | Low | B62 |
NVFP4 | 4 | 7.8 GB | Medium | B63 |
Q4_K_M | 4 | 8.5 GB | Medium | B62 |
Q5_K_M | 5 | 10.1 GB | High | B62 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | B62 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Copy-paste commands to run Phi 3 Medium 14B on your machine.
Run
ollama run phi3:mediumUpgrade options
RTX 4000 Ada 20GBBudget pickRaises estimated decode speed by about 80%.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
RTX 3090 24GBBest valueRaises estimated decode speed by about 321%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIA upgradeRaises estimated decode speed by about 263%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Yes, NVIDIA A2 16GB can run Phi 3 Medium 14B with a B grade (Tight fit). Expected decode speed: 19.6 tok/s.
Phi 3 Medium 14B (14B parameters) requires approximately 14.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3 Medium 14B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA A2 16GB, Phi 3 Medium 14B achieves approximately 19.6 tokens per second decode speed with a time-to-first-token of 9859ms using Q4_K_M quantization.
For coding workloads, Phi 3 Medium 14B on NVIDIA A2 16GB receives a B grade with 19.6 tok/s and 24K context.
On NVIDIA A2 16GB, Phi 3 Medium 14B can safely use up to 24K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-3-medium-14b-on-a2-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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