Can Phi-4-reasoning-plus 14B run on NVIDIA V100 32GB?

YES — Runs Great

S92Excellent

Estimated from fit model

Phi-4-reasoning-plus 14B needs ~16.4 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~72 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 16.4 GB, 72.3 tok/s, Runs well

16.4 GB required32.0 GB available

51% VRAM used

Fit status

Runs well

Decode

72.3 tok/s

TTFT

2678 ms

Safe context

33K

Memory

16.4 GB / 32.0 GB

Memory breakdown

Weights9.0 GB

KV Cache3.1 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsPhi-4-reasoning-plus 14B on NVIDIA V100 32GB
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: 72.3 tok/s decode · 2.7s TTFT (warm) · 181 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	72.3 tok/s	1461 ms	33K
Coding	S	Runs well	72.3 tok/s	2678 ms	33K
Agentic Coding	S	Runs well	72.3 tok/s	3895 ms	33K
Reasoning	S	Runs well	72.3 tok/s	3165 ms	33K
RAG	S	Runs well	72.3 tok/s	4869 ms	33K

Quantization options

How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.7 GB	Low	A84
Q3_K_S	3	7.2 GB	Low	A84
NVFP4	4	8.2 GB	Medium	A85
Q4_K_M	4	9.0 GB	Medium	S85
Q5_K_M	5	10.6 GB	High	S86
Q6_K	6	12.1 GB	High	S87
Q8_0Best for your GPU	8	15.7 GB	Very High	S88
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

Your hardware

More models your NVIDIA V100 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	91.2 tok/s
Qwen 3.5 27B	27B	S	39.5 tok/s
Qwen 3.6 27B	27B	S	39.7 tok/s
Qwen 3.6 35B A3B	35B	S	76.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	94.3 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run Phi-4-reasoning-plus 14B?

Yes, NVIDIA V100 32GB can run Phi-4-reasoning-plus 14B with a S grade (Runs well). Expected decode speed: 72.3 tok/s.

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

Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 16.4 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 V100 32GB?

On NVIDIA V100 32GB, Phi-4-reasoning-plus 14B achieves approximately 72.3 tokens per second decode speed with a time-to-first-token of 2678ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Phi-4-reasoning-plus 14B for coding?

For coding workloads, Phi-4-reasoning-plus 14B on NVIDIA V100 32GB receives a S grade with 72.3 tok/s and 33K context.

What context window can Phi-4-reasoning-plus 14B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Phi-4-reasoning-plus 14B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Phi-4-reasoning-plus 14B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

72.3 tok/s

1461 ms

33K

Coding

Runs well

72.3 tok/s

2678 ms

33K

Agentic Coding

Runs well

72.3 tok/s

3895 ms

33K

Reasoning

Runs well

72.3 tok/s

3165 ms

33K

RAG

Runs well

72.3 tok/s

4869 ms

33K

Quantization options

How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.7 GB	Low	A84
Q3_K_S	3	7.2 GB	Low	A84
NVFP4	4	8.2 GB	Medium	A85
Q4_K_M	4	9.0 GB	Medium	S85
Q5_K_M	5	10.6 GB	High	S86
Q6_K	6	12.1 GB	High	S87
Q8_0Best for your GPU	8	15.7 GB	Very High	S88
F16	16	30.1 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

91.2 tok/s

Qwen 3.5 27B

27B

39.5 tok/s

Qwen 3.6 27B

27B

39.7 tok/s

Qwen 3.6 35B A3B

35B

76.6 tok/s

Qwen3-VL 30B A3B Instruct

30B

94.3 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run Phi-4-reasoning-plus 14B?

Yes, NVIDIA V100 32GB can run Phi-4-reasoning-plus 14B with a S grade (Runs well). Expected decode speed: 72.3 tok/s.

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

Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 16.4 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 V100 32GB?

On NVIDIA V100 32GB, Phi-4-reasoning-plus 14B achieves approximately 72.3 tokens per second decode speed with a time-to-first-token of 2678ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Phi-4-reasoning-plus 14B for coding?

For coding workloads, Phi-4-reasoning-plus 14B on NVIDIA V100 32GB receives a S grade with 72.3 tok/s and 33K context.

What context window can Phi-4-reasoning-plus 14B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Phi-4-reasoning-plus 14B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.