Phi-4 14B needs ~13.7 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~12 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
1.7 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1.1 GB host RAM)
Decode
11.8 tok/s
TTFT
16441 ms
Safe context
7K
Memory
13.7 GB / 12.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload (needs ~0.1 GB host RAM) | 15.1 tok/s | 6993 ms | 7K |
| Coding | B | Very compromised (needs ~1.1 GB host RAM) | 11.8 tok/s | 16441 ms | 7K |
| Agentic Coding | F | Too heavy | 7.7 tok/s | 36526 ms | 7K |
| Reasoning | B | Very compromised (needs ~1.1 GB host RAM) | 11.8 tok/s | 19430 ms | 7K |
| RAG | F | Too heavy | 7.7 tok/s | 45658 ms | 7K |
How Phi-4 14B (14B params) fits at each quantization level on Intel Arc A730M 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A84 |
Q3_K_S | 3 | 6.9 GB | Low | A84 |
NVFP4 | 4 | 7.8 GB | Medium | A83 |
Q4_K_MBest for your GPU | 4 | 8.5 GB | Medium | A83 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Copy-paste commands to run Phi-4 14B on your machine.
Run
ollama run phi4Upgrade options
Intel Arc A770 16GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 169%.
~$349 MSRP
Intel Arc Pro B50 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 29%.
~$399 MSRP
Intel Arc Pro B60 24GBIntel upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 163%.
~$599 MSRP
Yes, Intel Arc A730M 12GB can run Phi-4 14B with a B grade (Very compromised (needs ~1.1 GB host RAM)). Expected decode speed: 11.8 tok/s.
Phi-4 14B (14B parameters) requires approximately 13.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4 14B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A730M 12GB, Phi-4 14B achieves approximately 11.8 tokens per second decode speed with a time-to-first-token of 16441ms using Q4_K_M quantization.
For coding workloads, Phi-4 14B on Intel Arc A730M 12GB receives a B grade with 11.8 tok/s and 7K context.
On Intel Arc A730M 12GB, Phi-4 14B can safely use up to 7K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
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