Can Phi 4 reasoning vision 15B run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

C47Usable

Estimated from fit model

Phi 4 reasoning vision 15B needs ~20.1 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~184 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) — 20.1 GB, 183.6 tok/s, Runs well

20.1 GB required80.0 GB available

25% VRAM used

Fit status

Runs well

Decode

183.6 tok/s

TTFT

1054 ms

Safe context

561K

Memory

20.1 GB / 80.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsPhi 4 reasoning vision 15B on NVIDIA H100 PCIe 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: 183.6 tok/s decode · 1.1s TTFT (warm) · 459 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	C	Runs well	183.6 tok/s	575 ms	561K
Coding	C	Runs well	183.6 tok/s	1054 ms	561K
Agentic Coding	C	Runs well	183.6 tok/s	1534 ms	561K
Reasoning	C	Runs well	183.6 tok/s	1246 ms	561K
RAG	C	Runs well	183.6 tok/s	1917 ms	561K

Inference speed

Phi 4 reasoning vision 15B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi 4 reasoning vision 15B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 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	131.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	83.7	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	75.5	Fits
RTX 3090 24GB	24 GB	Q4_K_M	71.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	68.3	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	60.9	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	50.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	48.1	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	34.7	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	34.7	Fits
RTX 4070 12GB	12 GB	Q4_K_M	26.7	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	26.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	24.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	15.7	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	5.9	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 vision 15B (15B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	D40
Q3_K_S	3	7.4 GB	Low	D40
NVFP4	4	8.4 GB	Medium	D40
Q4_K_M	4	9.2 GB	Medium	D40
Q5_K_M	5	10.8 GB	High	C40
Q6_K	6	12.3 GB	High	C40
Q8_0	8	16.1 GB	Very High	C41
F16Best for your GPU	16	30.7 GB	Maximum	C44

Get started

Copy-paste commands to run Phi 4 reasoning vision 15B on your machine.

Run

lms load hf-jamesburton--phi-4-reasoning-vision-15b-gguf && lms server start

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run Phi 4 reasoning vision 15B?

Yes, NVIDIA H100 PCIe 80GB can run Phi 4 reasoning vision 15B with a C grade (Runs well). Expected decode speed: 183.6 tok/s.

How much VRAM does Phi 4 reasoning vision 15B need?

Phi 4 reasoning vision 15B (15B parameters) requires approximately 20.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 4 reasoning vision 15B?

The recommended quantization for Phi 4 reasoning vision 15B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 4 reasoning vision 15B run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Phi 4 reasoning vision 15B achieves approximately 183.6 tokens per second decode speed with a time-to-first-token of 1054ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Phi 4 reasoning vision 15B for coding?

For coding workloads, Phi 4 reasoning vision 15B on NVIDIA H100 PCIe 80GB receives a C grade with 183.6 tok/s and 561K context.

What context window can Phi 4 reasoning vision 15B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Phi 4 reasoning vision 15B can safely use up to 561K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H100 PCIe 80GB See all hardware for Phi 4 reasoning vision 15B

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