Will It Run AI

Can Phi-4-reasoning-plus 14B run on RTX 4090 Laptop 16GB?

YES — Tight Fit

S92Excellent
Estimated from fit model

Phi-4-reasoning-plus 14B needs ~14.5 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~57 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 14.5 GB, 56.9 tok/s, Tight fit
14.5 GB required16.0 GB available
91% VRAM used

Fit status

Tight fit

Decode

56.9 tok/s

TTFT

3400 ms

Safe context

24K

Memory

14.5 GB / 16.0 GB

Memory breakdown

Weights9.0 GB
KV Cache3.1 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsPhi-4-reasoning-plus 14B on RTX 4090 Laptop 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: 56.9 tok/s decode · 3.4s TTFT (warm) · 142 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 well56.9 tok/s1854 ms24K
CodingSTight fit56.9 tok/s3400 ms24K
Agentic CodingAVery compromised (needs ~0.8 GB host RAM)35.1 tok/s8029 ms24K
ReasoningSTight fit56.9 tok/s4018 ms24K
RAGAVery compromised31.6 tok/s11123 ms24K

Quantization options

How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on RTX 4090 Laptop 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 4090 Laptop 16GB run Phi-4-reasoning-plus 14B?

Yes, RTX 4090 Laptop 16GB can run Phi-4-reasoning-plus 14B with a S grade (Tight fit). Expected decode speed: 56.9 tok/s.

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

Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 14.5 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 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Phi-4-reasoning-plus 14B achieves approximately 56.9 tokens per second decode speed with a time-to-first-token of 3400ms using Q4_K_M quantization.

Can RTX 4090 Laptop 16GB run Phi-4-reasoning-plus 14B for coding?

For coding workloads, Phi-4-reasoning-plus 14B on RTX 4090 Laptop 16GB receives a S grade with 56.9 tok/s and 24K context.

What context window can Phi-4-reasoning-plus 14B use on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Phi-4-reasoning-plus 14B can safely use up to 24K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for RTX 4090 Laptop 16GBSee all hardware for Phi-4-reasoning-plus 14B
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