Phi-4-reasoning-plus 14B needs ~13.7 GB but Intel Arc A550M 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
5.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.2 tok/s
TTFT
61049 ms
Safe context
4K
Memory
13.7 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 13.7 GB, but this setup only exposes 8.0 GB of usable VRAM.
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.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
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 | F | Too heavy | 4.1 tok/s | 25980 ms | 4K |
| Coding | F | Too heavy | 3.2 tok/s | 61049 ms | 4K |
| Agentic Coding | F | Too heavy | 2.1 tok/s | 135529 ms | 4K |
| Reasoning | F | Too heavy | 3.2 tok/s | 72149 ms | 4K |
| RAG | F | Too heavy | 2.1 tok/s | 169411 ms | 4K |
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on Intel Arc A550M 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.7 GB | Low | F0 |
Q3_K_S | 3 | 7.2 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
Intel Arc B580 12GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 350%.
~$249 MSRP
Intel Arc A770 16GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$349 MSRP
RTX 4090 24GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,599 MSRP
Intel Arc A730M 12GBIntel upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 228%.
No, Phi-4-reasoning-plus 14B requires more memory than Intel Arc A550M 8GB provides.
Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 13.7 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 A550M 8GB, Phi-4-reasoning-plus 14B achieves approximately 3.2 tokens per second decode speed with a time-to-first-token of 61049ms using Q4_K_M quantization.
For coding workloads, Phi-4-reasoning-plus 14B on Intel Arc A550M 8GB receives a F grade with 3.2 tok/s and 4K context.
On Intel Arc A550M 8GB, Phi-4-reasoning-plus 14B can safely use up to 4K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-4-reasoning-plus-14b-on-arc-a550m-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 9.0 GB | Medium | F0 |
Q5_K_M | 5 | 10.6 GB | High | F0 |
Q6_K | 6 | 12.1 GB | High | F0 |
Q8_0 | 8 | 15.7 GB | Very High | F0 |
F16 | 16 | 30.1 GB | Maximum | F0 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
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.
