Will It Run AI

Can Phi 4 reasoning vision 15B run on Intel Arc Pro B60 24GB?

YES — Runs Great

C51Usable
Estimated from fit model

Phi 4 reasoning vision 15B needs ~14.2 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~27 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 14.2 GB, 26.9 tok/s, Runs well
14.2 GB required24.0 GB available
59% VRAM used

Fit status

Runs well

Decode

26.9 tok/s

TTFT

7194 ms

Safe context

105K

Memory

14.2 GB / 24.0 GB

Memory breakdown

Weights9.2 GB
KV Cache1.8 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsPhi 4 reasoning vision 15B on Intel Arc Pro B60 24GB
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: 26.9 tok/s decode · 7.2s TTFT (warm) · 67 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well26.9 tok/s3924 ms105K
CodingCRuns well26.9 tok/s7194 ms105K
Agentic CodingCRuns well26.9 tok/s10464 ms105K
ReasoningCRuns well26.9 tok/s8502 ms105K
RAGCRuns well26.9 tok/s13080 ms105K

Quantization options

How Phi 4 reasoning vision 15B (15B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.9 GB
LowC46
Q3_K_S
3
7.4 GB
LowC47
NVFP4
4
8.4 GB
MediumC47
Q4_K_M
4
9.2 GB
MediumC48
Q5_K_M
5
10.8 GB
HighC49
Q6_K
6
12.3 GB
HighC50
Q8_0Best for your GPU
8
16.1 GB
Very HighC50
F16
16
30.7 GB
MaximumF0

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

アップグレードオプション

Phi 4 reasoning vision 15Bを快適に動かすハードウェア

NVIDIARTX 5090 32GBコスパ重視
32 GB VRAM (+8)1792 GB/s (+1336)
C
Raises estimated decode speed by about 361%.124 tok/sデコード

Raises estimated decode speed by about 361%.

Adds memory headroom for longer context windows and future model growth.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

〜$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBコスパ最良
32 GB VRAM (+8)896 GB/s (+440)
C
Raises estimated decode speed by about 206%.82.3 tok/sデコード

Raises estimated decode speed by about 206%.

Adds memory headroom for longer context windows and future model growth.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

〜$2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Phi 4 reasoning vision 15B?

Yes, Intel Arc Pro B60 24GB can run Phi 4 reasoning vision 15B with a C grade (Runs well). Expected decode speed: 26.9 tok/s.

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

Phi 4 reasoning vision 15B (15B parameters) requires approximately 14.2 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 Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Phi 4 reasoning vision 15B achieves approximately 26.9 tokens per second decode speed with a time-to-first-token of 7194ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Phi 4 reasoning vision 15B for coding?

For coding workloads, Phi 4 reasoning vision 15B on Intel Arc Pro B60 24GB receives a C grade with 26.9 tok/s and 105K context.

What context window can Phi 4 reasoning vision 15B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Phi 4 reasoning vision 15B can safely use up to 105K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Phi 4 reasoning vision 15B feels slow on Intel Arc Pro B60 24GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Intel Arc Pro B60 24GB for Phi 4 reasoning vision 15B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc Pro B60 24GBSee all hardware for Phi 4 reasoning vision 15B
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