Will It Run AI

Can Phi-4 Mini Reasoning 4B run on RTX 3080 10GB?

YES — Runs Great

S90Excellent
Estimated from fit model

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.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Share:

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 6.0 GB, 53.2 tok/s, Runs well
6.0 GB required10.0 GB available
60% VRAM used

Fit status

Runs well

Decode

53.2 tok/s

TTFT

3639 ms

Safe context

60K

Memory

6.0 GB / 10.0 GB

Memory breakdown

Weights2.3 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom1.0 GB

See how fast it feels

See how fast it feelsPhi-4 Mini Reasoning 4B on RTX 3080 10GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 53.2 tok/s decode · 3.6s TTFT (warm) · 133 tok/s prefill

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

WorkloadGradeFitDecodeTTFTContext
ChatSRuns well53.2 tok/s1985 ms60K
CodingSRuns well53.2 tok/s3639 ms60K
Agentic CodingSRuns well53.2 tok/s5293 ms60K
ReasoningSRuns well53.2 tok/s4301 ms60K
RAGSRuns well53.2 tok/s6617 ms60K

Quantization options

How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
1.5 GB
LowS86
Q3_K_S
3
1.9 GB
LowS87
NVFP4
4
2.1 GB
MediumS87
Q4_K_M
4
2.3 GB
MediumS87
Q5_K_M
5
2.7 GB
HighS88
Q6_K
6
3.1 GB
HighS88
Q8_0Best for your GPU
8
4.1 GB
Very HighS90
F16
16
7.8 GB
MaximumF0

Get started

Copy-paste commands to run Phi-4 Mini Reasoning 4B on your machine.

Run

ollama run phi4-mini

Your hardware

More models your RTX 3080 10GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen 3.5 9B9BS113.1 tok/s
AlibabaQwen 3.5 4B4BS56 tok/s
AlibabaQwen 3 8B8BS112 tok/s

Frequently asked questions

Can RTX 3080 10GB run Phi-4 Mini Reasoning 4B?

Yes, RTX 3080 10GB can run Phi-4 Mini Reasoning 4B with a S grade (Runs well). Expected decode speed: 53.2 tok/s.

How much VRAM does Phi-4 Mini Reasoning 4B need?

Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 6.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi-4 Mini Reasoning 4B?

The recommended quantization for Phi-4 Mini Reasoning 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi-4 Mini Reasoning 4B run at on RTX 3080 10GB?

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.

Can RTX 3080 10GB run Phi-4 Mini Reasoning 4B for coding?

For coding workloads, Phi-4 Mini Reasoning 4B on RTX 3080 10GB receives a S grade with 53.2 tok/s and 60K context.

What context window can Phi-4 Mini Reasoning 4B use on RTX 3080 10GB?

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.

See all results for RTX 3080 10GBSee all hardware for Phi-4 Mini Reasoning 4B
Embed this result▼

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: