Will It Run AI

Can Phi-4 Mini Reasoning 4B run on RTX 4050 Laptop 6GB?

YES — Tight Fit

S90Excellent
Estimated from fit model

Phi-4 Mini Reasoning 4B needs ~5.3 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q4_K_M quantization, expect ~60 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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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) — 5.3 GB, 59.8 tok/s, Tight fit
5.3 GB required6.0 GB available
88% VRAM used

Fit status

Tight fit

Decode

59.8 tok/s

TTFT

3237 ms

Safe context

24K

Memory

5.3 GB / 6.0 GB

Memory breakdown

Weights2.3 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom0.6 GB

See how fast it feels

See how fast it feelsPhi-4 Mini Reasoning 4B on RTX 4050 Laptop 6GB
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: 59.8 tok/s decode · 3.2s TTFT (warm) · 150 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 well59.8 tok/s1766 ms24K
CodingSTight fit59.8 tok/s3237 ms24K
Agentic CodingAVery compromised (needs ~0.3 GB host RAM)35.0 tok/s8039 ms24K
ReasoningSTight fit59.8 tok/s3826 ms24K
RAGAVery compromised (needs ~0.3 GB host RAM)35.0 tok/s10049 ms24K

Quantization options

How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
1.5 GB
LowS91
Q3_K_S
3
1.9 GB
LowS92
NVFP4
4
2.1 GB
MediumS92
Q4_K_M
4
2.3 GB
MediumS91
Q5_K_M
5
2.7 GB
HighS91
Q6_KBest for your GPU
6
3.1 GB
HighS91
Q8_0
8
4.1 GB
Very HighF0
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 4050 Laptop 6GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen 3.5 4B4BS40.6 tok/s

Frequently asked questions

Can RTX 4050 Laptop 6GB run Phi-4 Mini Reasoning 4B?

Yes, RTX 4050 Laptop 6GB can run Phi-4 Mini Reasoning 4B with a S grade (Tight fit). Expected decode speed: 59.8 tok/s.

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

Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 5.3 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 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, Phi-4 Mini Reasoning 4B achieves approximately 59.8 tokens per second decode speed with a time-to-first-token of 3237ms using Q4_K_M quantization.

Can RTX 4050 Laptop 6GB run Phi-4 Mini Reasoning 4B for coding?

For coding workloads, Phi-4 Mini Reasoning 4B on RTX 4050 Laptop 6GB receives a S grade with 59.8 tok/s and 24K context.

What context window can Phi-4 Mini Reasoning 4B use on RTX 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, Phi-4 Mini Reasoning 4B can safely use up to 24K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4050 Laptop 6GBSee all hardware for Phi-4 Mini Reasoning 4B
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