Will It Run AI

Can Phi-4-reasoning-plus 14B run on RTX 5080 16GB?

YES — Tight Fit

S93Excellent
Estimated from fit model

Phi-4-reasoning-plus 14B needs ~14.8 GB VRAM. RTX 5080 16GB has 16.0 GB. With Q4_K_M quantization, expect ~75 tok/s.

Runtime: OllamaCapacity: TightBandwidth: HighStack: BasicBottleneck: Balanced
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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) — 14.8 GB, 74.8 tok/s, Tight fit
14.8 GB required16.0 GB available
93% VRAM used

Fit status

Tight fit

Decode

74.8 tok/s

TTFT

2588 ms

Safe context

22K

Memory

14.8 GB / 16.0 GB

Memory breakdown

Weights9.0 GB
KV Cache3.1 GB
Runtime1.2 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsPhi-4-reasoning-plus 14B on RTX 5080 16GB
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: 74.8 tok/s decode · 2.6s TTFT (warm) · 187 tok/s prefill

What limits this setup

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.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatSTight fit74.8 tok/s1412 ms22K
CodingSTight fit74.8 tok/s2588 ms22K
Agentic CodingAVery compromised (needs ~0.9 GB host RAM)45.7 tok/s6168 ms22K
ReasoningSTight fit74.8 tok/s3059 ms22K
RAGAVery compromised (needs ~0.9 GB host RAM)45.7 tok/s7710 ms22K

Quantization options

How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on RTX 5080 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.7 GB
LowS89
Q3_K_S
3
7.2 GB
LowS91
NVFP4
4
8.2 GB
MediumS91
Q4_K_M
4
9.0 GB
MediumS91
Q5_K_M
5
10.6 GB
HighS91
Q6_KBest for your GPU
6
12.1 GB
HighS90
Q8_0
8
15.7 GB
Very HighF0
F16
16
30.1 GB
MaximumF0

Get started

Copy-paste commands to run Phi-4-reasoning-plus 14B on your machine.

Run

ollama run phi4-reasoning

Frequently asked questions

Can RTX 5080 16GB run Phi-4-reasoning-plus 14B?

Yes, RTX 5080 16GB can run Phi-4-reasoning-plus 14B with a S grade (Tight fit). Expected decode speed: 74.8 tok/s.

How much VRAM does Phi-4-reasoning-plus 14B need?

Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 14.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi-4-reasoning-plus 14B?

The recommended quantization for Phi-4-reasoning-plus 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi-4-reasoning-plus 14B run at on RTX 5080 16GB?

On RTX 5080 16GB, Phi-4-reasoning-plus 14B achieves approximately 74.8 tokens per second decode speed with a time-to-first-token of 2588ms using Q4_K_M quantization.

Can RTX 5080 16GB run Phi-4-reasoning-plus 14B for coding?

For coding workloads, Phi-4-reasoning-plus 14B on RTX 5080 16GB receives a S grade with 74.8 tok/s and 22K context.

What context window can Phi-4-reasoning-plus 14B use on RTX 5080 16GB?

On RTX 5080 16GB, Phi-4-reasoning-plus 14B can safely use up to 22K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Phi-4-reasoning-plus 14B feels slow on RTX 5080 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 5080 16GBSee all hardware for Phi-4-reasoning-plus 14B
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