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Phi-4 14B needs ~13.7 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~15 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
16.1 tok/s
TTFT
12025 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) | 20.5 tok/s | 5154 ms | 7K |
| Coding | B | Very compromised | 15.0 tok/s | 12927 ms | 7K |
| Agentic Coding | F | Too heavy | 10.7 tok/s | 26369 ms | 7K |
| Reasoning | B | Very compromised (needs ~1.1 GB host RAM) | 16.1 tok/s | 14212 ms | 7K |
| RAG | F | Too heavy | 9.9 tok/s | 35434 ms |
How Phi-4 14B (14B params) fits at each quantization level on Intel Arc B580 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 |
Copy-paste commands to run Phi-4 14B on your machine.
Run
ollama run phi4Upgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 97%.
~$349 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$399 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 93%.
~$599 MSRP
| 7K |
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 |
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.
