Can Phi 4 Mini 4B run on Intel Arc A550M 8GB?
YES — Runs Great
Phi 4 Mini 4B needs ~5.6 GB VRAM. Intel Arc A550M 8GB has 8.0 GB. With Q4_K_M quantization, expect ~45 tok/s.
Operating mode
Choose the run profile you care about
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
48.4 tok/s
TTFT
4004 ms
Safe context
42K
Memory
5.6 GB / 8.0 GB
Memory breakdown
See how fast it feels
What limits this setup
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.
Best improvement path
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.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 45.0 tok/s | 2348 ms | 42K |
| Coding | A | Runs well | 45.0 tok/s | 4304 ms | 42K |
| Agentic Coding | A | Tight fit | 45.0 tok/s | 6260 ms | 42K |
| Reasoning | A | Runs well | 45.0 tok/s | 5086 ms | 42K |
| RAG | A | Tight fit | 45.0 tok/s | 7825 ms | 42K |
Quantization options
How Phi 4 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 | A72 |
Q3_K_S | 3 | 2.0 GB | Low | A72 |
NVFP4 | 4 | 2.2 GB | Medium | A73 |
Q4_K_M | 4 | 2.4 GB | Medium | A73 |
Q5_K_M | 5 | 2.9 GB | High | A74 |
Q6_K | 6 | 3.3 GB | High | A74 |
Q8_0Best for your GPU | 8 | 4.3 GB | Very High | A74 |
F16 | 16 | 8.2 GB | Maximum | F0 |
Get started
Copy-paste commands to run Phi 4 Mini 4B on your machine.
Run
ollama run phi4-miniYour hardware
More models your Intel Arc A550M 8GB can run
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 9B | A | 11.5 tok/s | ||
| 8B | A | 14.9 tok/s | ||
| 8B | A | 15.8 tok/s | ||
| 8B | A | 15.8 tok/s | ||
| 8B | B | 14.9 tok/s |
Frequently asked questions
Can Intel Arc A550M 8GB run Phi 4 Mini 4B?
Yes, Intel Arc A550M 8GB can run Phi 4 Mini 4B with a A grade (Runs well). Expected decode speed: 45.0 tok/s.
How much VRAM does Phi 4 Mini 4B need?
Phi 4 Mini 4B (4B parameters) requires approximately 5.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Phi 4 Mini 4B?
The recommended quantization for Phi 4 Mini 4B is Q4_K_M, which balances quality and memory efficiency.
What speed will Phi 4 Mini 4B run at on Intel Arc A550M 8GB?
On Intel Arc A550M 8GB, Phi 4 Mini 4B achieves approximately 45.0 tokens per second decode speed with a time-to-first-token of 4304ms using Q4_K_M quantization.
Can Intel Arc A550M 8GB run Phi 4 Mini 4B for coding?
For coding workloads, Phi 4 Mini 4B on Intel Arc A550M 8GB receives a A grade with 45.0 tok/s and 42K context.
What context window can Phi 4 Mini 4B use on Intel Arc A550M 8GB?
On Intel Arc A550M 8GB, Phi 4 Mini 4B can safely use up to 42K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
What should I upgrade first if Phi 4 Mini 4B feels slow on Intel Arc A550M 8GB?
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.
Would CUDA be a better path than Intel Arc A550M 8GB for Phi 4 Mini 4B?
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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