Will It Run AI

Can Phi-4-reasoning-plus 14B run on NVIDIA B200 180GB?

YES — Runs Great

S85Excellent
Estimated from fit model

Phi-4-reasoning-plus 14B needs ~31.2 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~206 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 31.2 GB, 205.8 tok/s, Runs well
31.2 GB required180.0 GB available
17% VRAM used

Fit status

Runs well

Decode

205.8 tok/s

TTFT

941 ms

Safe context

33K

Memory

31.2 GB / 180.0 GB

Memory breakdown

Weights9.0 GB
KV Cache3.1 GB
Runtime1.2 GB
Headroom18.0 GB

See how fast it feels

See how fast it feelsPhi-4-reasoning-plus 14B on NVIDIA B200 180GB
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: 205.8 tok/s decode · 941ms TTFT (warm) · 515 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 well205.8 tok/s513 ms33K
CodingSRuns well205.8 tok/s941 ms33K
Agentic CodingSRuns well205.8 tok/s1368 ms33K
ReasoningSRuns well205.8 tok/s1112 ms33K
RAGSRuns well205.8 tok/s1710 ms33K

Quantization options

How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.7 GB
LowA77
Q3_K_S
3
7.2 GB
LowA77
NVFP4
4
8.2 GB
MediumA77
Q4_K_M
4
9.0 GB
MediumA77
Q5_K_M
5
10.6 GB
HighA77
Q6_K
6
12.1 GB
HighA77
Q8_0
8
15.7 GB
Very HighA77
F16Best for your GPU
16
30.1 GB
MaximumA78

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 B200 180GB can run

ModelParamsGradeDecodeCapabilities
MistralDevstral 2 123B Instruct123BS97.4 tok/s
AlibabaQwen3-Coder 30B A3B Instruct30.5BS1016.1 tok/s
AlibabaQwen 3.5 27B27BS378 tok/s
AlibabaQwen 3.6 27B27BS378 tok/s
AlibabaQwen 3.5 122B A10B122BS270.2 tok/s

Frequently asked questions

Can NVIDIA B200 180GB run Phi-4-reasoning-plus 14B?

Yes, NVIDIA B200 180GB can run Phi-4-reasoning-plus 14B with a S grade (Runs well). Expected decode speed: 205.8 tok/s.

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

Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 31.2 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 B200 180GB?

On NVIDIA B200 180GB, Phi-4-reasoning-plus 14B achieves approximately 205.8 tokens per second decode speed with a time-to-first-token of 941ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Phi-4-reasoning-plus 14B for coding?

For coding workloads, Phi-4-reasoning-plus 14B on NVIDIA B200 180GB receives a S grade with 205.8 tok/s and 33K context.

What context window can Phi-4-reasoning-plus 14B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, 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 B200 180GBSee all hardware for Phi-4-reasoning-plus 14B
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