Can Phi-4-reasoning-plus 14B run on NVIDIA T4 16GB?

YES — Tight Fit

S89Excellent

Estimated from fit model

Phi-4-reasoning-plus 14B needs ~14.8 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced

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, 24.9 tok/s, Tight fit

14.8 GB required16.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

24.9 tok/s

TTFT

7764 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 NVIDIA T4 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: 24.9 tok/s decode · 7.8s TTFT (warm) · 62 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	24.9 tok/s	4235 ms	22K
Coding	S	Tight fit	24.9 tok/s	7764 ms	22K
Agentic Coding	A	Very compromised (needs ~0.9 GB host RAM)	14.3 tok/s	19737 ms	22K
Reasoning	S	Tight fit	24.9 tok/s	9176 ms	22K
RAG	A	Very compromised (needs ~0.9 GB host RAM)	14.3 tok/s	24671 ms	22K

Inference speed

Phi-4-reasoning-plus 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi-4-reasoning-plus 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~144 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	143.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	91.8	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	82.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	78.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	75.5	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	66.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	55.6	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	52.7	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	37.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	37.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	28.8	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	26.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	24.9	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	14.6	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	5.5	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.7 GB	Low	S89
Q3_K_S	3	7.2 GB	Low	S91
NVFP4	4	8.2 GB	Medium	S91
Q4_K_M	4	9.0 GB	Medium	S91
Q5_K_M	5	10.6 GB	High	S91
Q6_KBest for your GPU	6	12.1 GB	High	S90
Q8_0	8	15.7 GB	Very High	F0
F16	16	30.1 GB	Maximum	F0

Get started

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

Run

ollama run phi4-reasoning

Frequently asked questions

Can NVIDIA T4 16GB run Phi-4-reasoning-plus 14B?

Yes, NVIDIA T4 16GB can run Phi-4-reasoning-plus 14B with a S grade (Tight fit). Expected decode speed: 24.9 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 NVIDIA T4 16GB?

On NVIDIA T4 16GB, Phi-4-reasoning-plus 14B achieves approximately 24.9 tokens per second decode speed with a time-to-first-token of 7764ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run Phi-4-reasoning-plus 14B for coding?

For coding workloads, Phi-4-reasoning-plus 14B on NVIDIA T4 16GB receives a S grade with 24.9 tok/s and 22K context.

What context window can Phi-4-reasoning-plus 14B use on NVIDIA T4 16GB?

On NVIDIA T4 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 NVIDIA T4 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 NVIDIA T4 16GB See all hardware for Phi-4-reasoning-plus 14B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/phi-4-reasoning-plus-14b-on-t4-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: