RTX 5060 Ti 16GBBudget pick16 GB VRAM (+4)
A
Adds memory headroom for longer context windows and future model growth.53.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Phi 3 Mini 3.8B needs ~10.6 GB VRAM. RTX 3080 12GB has 12.0 GB. With Q4_K_M quantization, expect ~53 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
53.2 tok/s
TTFT
3639 ms
Safe context
20K
Memory
10.6 GB / 12.0 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 | A | Runs well | 53.2 tok/s | 1985 ms | 20K |
| Coding | B | Tight fit | 53.2 tok/s | 3639 ms | 20K |
| Agentic Coding | F | Too heavy | 53.2 tok/s | 5293 ms | 20K |
| Reasoning | B | Tight fit | 53.2 tok/s | 4301 ms | 20K |
| RAG | F | Too heavy | 53.2 tok/s | 6617 ms | 20K |
How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on RTX 3080 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | B65 |
Q3_K_S | 3 | 1.9 GB | Low | B65 |
NVFP4 | 4 | 2.1 GB | Medium | B65 |
Q4_K_M | 4 | 2.3 GB | Medium | B65 |
Q5_K_M | 5 | 2.7 GB | High | B66 |
Q6_K | 6 | 3.1 GB | High | B66 |
Q8_0 | 8 | 4.1 GB | Very High | B68 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | B69 |
Copy-paste commands to run Phi 3 Mini 3.8B on your machine.
Run
ollama run phi3:miniUpgrade options
RTX 5060 Ti 16GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBBest valueAdds memory headroom for longer context windows and future model growth.
~$499 MSRP
RTX 2000 Ada 16GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$625 MSRP
Yes, RTX 3080 12GB can run Phi 3 Mini 3.8B with a B grade (Tight fit). Expected decode speed: 53.2 tok/s.
Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3080 12GB, Phi 3 Mini 3.8B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.
For coding workloads, Phi 3 Mini 3.8B on RTX 3080 12GB receives a B grade with 53.2 tok/s and 20K context.
On RTX 3080 12GB, Phi 3 Mini 3.8B can safely use up to 20K 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-mini-3.8b-on-rtx-3080-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: