Can Phi-4-reasoning-plus 14B run on NVIDIA H100 80GB?

YES — Runs Great

S87Excellent

Estimated from fit model

Phi-4-reasoning-plus 14B needs ~21.2 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~206 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) — 21.2 GB, 205.8 tok/s, Runs well

21.2 GB required80.0 GB available

27% VRAM used

Fit status

Runs well

Decode

205.8 tok/s

TTFT

941 ms

Safe context

33K

Memory

21.2 GB / 80.0 GB

Memory breakdown

Weights9.0 GB

KV Cache3.1 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsPhi-4-reasoning-plus 14B on NVIDIA H100 80GB
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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	205.8 tok/s	513 ms	33K
Coding	S	Runs well	205.8 tok/s	941 ms	33K
Agentic Coding	S	Runs well	205.8 tok/s	1368 ms	33K
Reasoning	S	Runs well	205.8 tok/s	1112 ms	33K
RAG	S	Runs well	205.8 tok/s	1710 ms	33K

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

Quantization options

How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.7 GB	Low	A79
Q3_K_S	3	7.2 GB	Low	A80
NVFP4	4

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 H100 80GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	A	28.9 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

Can NVIDIA H100 80GB run Phi-4-reasoning-plus 14B?

Yes, NVIDIA H100 80GB 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 21.2 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 H100 80GB?

On NVIDIA H100 80GB, 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 H100 80GB run Phi-4-reasoning-plus 14B for coding?

For coding workloads, Phi-4-reasoning-plus 14B on NVIDIA H100 80GB receives a S grade with 205.8 tok/s and 33K context.

What context window can Phi-4-reasoning-plus 14B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, 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 H100 80GB See all hardware for Phi-4-reasoning-plus 14B

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