MacBook Pro M3 Pro 36GBBudget pick
36 GB Unified (+12)
B
This setup is broadly balanced for this model.47.2 tok/s decode
~$1,999 MSRP
Phi 3 Mini 3.8B needs ~11.5 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~53 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
53.2 tok/s
TTFT
3639 ms
Safe context
50K
Memory
11.5 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 | B | Runs well | 53.2 tok/s | 1985 ms | 50K |
| Coding | B | Runs well | 53.2 tok/s | 3639 ms | 50K |
| Agentic Coding | A | Runs well | 53.2 tok/s | 5293 ms | 50K |
| Reasoning | B | Runs well | 53.2 tok/s | 4301 ms | 50K |
| RAG | A | Runs well | 53.2 tok/s | 6617 ms | 50K |
How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | B61 |
Q3_K_S | 3 | 1.9 GB | Low | B61 |
NVFP4 | 4 | 2.1 GB | Medium | B61 |
Q4_K_M | 4 | 2.3 GB | Medium | B61 |
Q5_K_M | 5 | 2.7 GB | High | B61 |
Q6_K | 6 | 3.1 GB | High | B62 |
Q8_0 | 8 | 4.1 GB | Very High | B62 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | B64 |
Copy-paste commands to run Phi 3 Mini 3.8B on your machine.
Run
ollama run phi3:miniUpgrade options
Yes, Intel Arc Pro B60 24GB can run Phi 3 Mini 3.8B with a B grade (Runs well). Expected decode speed: 53.2 tok/s.
Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 11.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B60 24GB, Phi 3 Mini 3.8B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.
For coding workloads, Phi 3 Mini 3.8B on Intel Arc Pro B60 24GB receives a B grade with 53.2 tok/s and 50K context.
On Intel Arc Pro B60 24GB, Phi 3 Mini 3.8B can safely use up to 50K tokens of context. The model's official context limit is 128K, 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.
<iframe src="https://willitrunai.com/embed/phi-3-mini-3.8b-on-arc-pro-b60-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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