Phi 3.5 Mini 4B needs ~9.5 GB VRAM. Intel Arc A550M 8GB has 8.0 GB. With Q3_K_S quantization, expect ~27 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
2.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
21.1 tok/s
TTFT
9178 ms
Safe context
11K
Memory
10.0 GB / 8.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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 | B | Tight fit | 45.0 tok/s | 2348 ms | 11K |
| Coding | F | Too heavy | 21.1 tok/s | 9178 ms | 11K |
| Agentic Coding | F | Too heavy | 8.0 tok/s | 35243 ms | 11K |
| Reasoning | F | Too heavy | 21.1 tok/s | 10846 ms | 11K |
| RAG | F | Too heavy | 8.0 tok/s | 44054 ms | 11K |
How Phi 3.5 Mini 4B (4B params) fits at each quantization level on Intel Arc A550M 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B67 |
Q3_K_S | 3 | 2.0 GB | Low | B67 |
NVFP4 | 4 |
Copy-paste commands to run Phi 3.5 Mini 4B on your machine.
Run
ollama run phi3.5Upgrade options
Intel Arc B570 10GBBudget 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.
~$219 MSRP
Intel Arc B580 12GBBest valueMakes 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.
~$249 MSRP
Intel Arc A770 16GBIntel upgradeMakes 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
Yes, Intel Arc A550M 8GB can run Phi 3.5 Mini 4B at Q3_K_S quantization (Very compromised (needs ~0.3 GB host RAM)). The recommended Q4_K_M requires 10.0 GB which exceeds available memory, but at Q3_K_S it needs only 9.5 GB. Expected decode speed: 27.1 tok/s.
Phi 3.5 Mini 4B (4B parameters) requires approximately 10.0 GB at Q4_K_M quantization. On Intel Arc A550M 8GB, it fits at Q3_K_S using 9.5 GB.
The recommended quantization is Q4_K_M, but on Intel Arc A550M 8GB the best fitting quantization is Q3_K_S, which uses 9.5 GB.
On Intel Arc A550M 8GB, Phi 3.5 Mini 4B achieves approximately 27.1 tokens per second decode speed with a time-to-first-token of 7147ms using Q3_K_S quantization.
For coding workloads, Phi 3.5 Mini 4B on Intel Arc A550M 8GB receives a F grade with 21.1 tok/s and 11K context.
On Intel Arc A550M 8GB, Phi 3.5 Mini 4B can safely use up to 12K tokens of context at Q3_K_S quantization. The model's official context limit is 128K, 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-3.5-mini-4b-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:
2.2 GB |
| Medium |
| B68 |
Q4_K_M | 4 | 2.4 GB | Medium | B68 |
Q5_K_M | 5 | 2.9 GB | High | B69 |
Q6_K | 6 | 3.3 GB | High | B69 |
Q8_0Best for your GPU | 8 | 4.3 GB | Very High | B69 |
F16 | 16 | 8.2 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.
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.