Phi-4-reasoning-plus 14B needs ~15.3 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~30 tok/s.
Operating mode
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.
Select quantization to explore
Fit status
Runs well
Decode
29.5 tok/s
TTFT
6558 ms
Safe context
33K
Memory
15.3 GB / 24.0 GB
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 29.5 tok/s | 3577 ms | 33K |
| Coding | S | Runs well | 29.5 tok/s | 6558 ms | 33K |
| Agentic Coding | S | Runs well | 29.5 tok/s | 9539 ms | 33K |
| Reasoning | S | Runs well | 29.5 tok/s | 7751 ms | 33K |
| RAG | S | Runs well | 29.5 tok/s | 11924 ms | 33K |
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.7 GB | Low | S86 |
Q3_K_S | 3 | 7.2 GB | Low | S86 |
NVFP4 | 4 | 8.2 GB | Medium | S87 |
Q4_K_M | 4 | 9.0 GB | Medium | S88 |
Q5_K_M | 5 | 10.6 GB | High | S89 |
Q6_K | 6 | 12.1 GB | High | S90 |
Q8_0Best for your GPU | 8 | 15.7 GB | Very High | S90 |
F16 | 16 | 30.1 GB | Maximum | F0 |
Copy-paste commands to run Phi-4-reasoning-plus 14B on your machine.
Run
ollama run phi4-reasoningYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 37.2 tok/s | |
| Qwen 3.5 27B | 27B | S | 16.1 tok/s | |
| Qwen 3.6 27B | 27B | S | 12.3 tok/s | |
| Qwen 3.6 35B A3B | 35B | A | 16.6 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 38.5 tok/s |
Yes, Intel Arc Pro B60 24GB can run Phi-4-reasoning-plus 14B with a S grade (Runs well). Expected decode speed: 29.5 tok/s.
Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 15.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4-reasoning-plus 14B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B60 24GB, Phi-4-reasoning-plus 14B achieves approximately 29.5 tokens per second decode speed with a time-to-first-token of 6558ms using Q4_K_M quantization.
For coding workloads, Phi-4-reasoning-plus 14B on Intel Arc Pro B60 24GB receives a S grade with 29.5 tok/s and 33K context.
On Intel Arc Pro B60 24GB, 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.
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.
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.
Paste this snippet into any page to show a live fit card.
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