Phi-4 Mini Reasoning 4B needs ~6.0 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
Fit status
Runs well
Decode
53.2 tok/s
TTFT
3639 ms
Safe context
60K
Memory
6.0 GB / 10.0 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 | S | Runs well | 53.2 tok/s | 1985 ms | 60K |
| Coding | S | Runs well | 53.2 tok/s | 3639 ms | 60K |
| Agentic Coding | S | Runs well | 53.2 tok/s | 5293 ms | 60K |
| Reasoning | S | Runs well | 53.2 tok/s | 4301 ms | 60K |
| RAG | S | Runs well | 53.2 tok/s | 6617 ms | 60K |
How Phi-4 Mini Reasoning 4B (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 | S86 |
Q3_K_S | 3 | 1.9 GB | Low | S87 |
NVFP4 | 4 | 2.1 GB | Medium | S87 |
Q4_K_M | 4 | 2.3 GB | Medium | S87 |
Q5_K_M | 5 | 2.7 GB | High | S88 |
Q6_K | 6 | 3.1 GB | High | S88 |
Q8_0Best for your GPU | 8 | 4.1 GB | Very High | S90 |
F16 | 16 | 7.8 GB | Maximum | F0 |
Copy-paste commands to run Phi-4 Mini Reasoning 4B on your machine.
Run
ollama run phi4-miniYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 113.1 tok/s | |
| Qwen 3.5 4B | 4B | S | 56 tok/s | |
| Qwen 3 8B | 8B | S | 112 tok/s |
Yes, RTX 3080 10GB can run Phi-4 Mini Reasoning 4B with a S grade (Runs well). Expected decode speed: 53.2 tok/s.
Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 6.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4 Mini Reasoning 4B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3080 10GB, Phi-4 Mini Reasoning 4B 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-4 Mini Reasoning 4B on RTX 3080 10GB receives a S grade with 53.2 tok/s and 60K context.
On RTX 3080 10GB, Phi-4 Mini Reasoning 4B can safely use up to 60K tokens of context. The model's official context limit is 131K, 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-4-mini-reasoning-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: