RTX 5060 Ti 16GBBudget pick16 GB VRAM (+6)
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 ~10.4 GB VRAM. RTX 3080 10GB has 10.0 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
0.4 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.1 GB host RAM)
Decode
53.2 tok/s
TTFT
3639 ms
Safe context
15K
Memory
10.4 GB / 10.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 53.2 tok/s | 1985 ms | 15K |
| Coding | B | Runs with offload (needs ~0.1 GB host RAM) | 53.2 tok/s | 3639 ms | 15K |
| Agentic Coding | F | Too heavy | 53.2 tok/s | 5293 ms | 15K |
| Reasoning | B | Runs with offload (needs ~0.1 GB host RAM) | 53.2 tok/s | 4301 ms | 15K |
| RAG | F | Too heavy | 53.2 tok/s | 6617 ms | 15K |
How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | B66 |
Q3_K_S | 3 | 1.9 GB | Low | B66 |
NVFP4 | 4 | 2.1 GB | Medium | B67 |
Q4_K_M | 4 | 2.3 GB | Medium | B67 |
Q5_K_M | 5 | 2.7 GB | High | B68 |
Q6_K | 6 | 3.1 GB | High | B68 |
Q8_0Best for your GPU | 8 | 4.1 GB | Very High | B70 |
F16 | 16 | 7.8 GB | Maximum | F0 |
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 10GB can run Phi 3 Mini 3.8B with a B grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 53.2 tok/s.
Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 10.4 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 10GB, 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 10GB receives a B grade with 53.2 tok/s and 15K context.
On RTX 3080 10GB, Phi 3 Mini 3.8B can safely use up to 15K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: