Intel Arc Pro B60 24GBBudget pick24 GB VRAM (+8)
B
Adds memory headroom for longer context windows and future model growth.31 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Phi 3 Medium 14B needs ~14.1 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~32 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
Fit status
Tight fit
Decode
31.7 tok/s
TTFT
6103 ms
Safe context
26K
Memory
14.1 GB / 16.0 GB
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.
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 | B | Runs well | 31.7 tok/s | 3329 ms | 26K |
| Coding | B | Tight fit | 31.7 tok/s | 6103 ms | 26K |
| Agentic Coding | C | Runs with offload (needs ~0.6 GB host RAM) | 20.6 tok/s | 13688 ms | 26K |
| Reasoning | B | Tight fit | 31.7 tok/s | 7213 ms | 26K |
| RAG | C | Runs with offload (needs ~0.6 GB host RAM) | 20.6 tok/s | 17110 ms | 26K |
How Phi 3 Medium 14B (14B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | B60 |
Q3_K_S | 3 | 6.9 GB | Low | B62 |
NVFP4 | 4 | 7.8 GB | Medium | B63 |
Q4_K_M | 4 | 8.5 GB | Medium | B62 |
Q5_K_M | 5 | 10.1 GB | High | B62 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | B62 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Copy-paste commands to run Phi 3 Medium 14B on your machine.
Run
ollama run phi3:mediumUpgrade options
Intel Arc Pro B60 24GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$599 MSRP
Raises estimated decode speed by about 91%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Yes, Intel Arc A770 16GB can run Phi 3 Medium 14B with a B grade (Tight fit). Expected decode speed: 31.7 tok/s.
Phi 3 Medium 14B (14B parameters) requires approximately 14.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi 3 Medium 14B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A770 16GB, Phi 3 Medium 14B achieves approximately 31.7 tokens per second decode speed with a time-to-first-token of 6103ms using Q4_K_M quantization.
For coding workloads, Phi 3 Medium 14B on Intel Arc A770 16GB receives a B grade with 31.7 tok/s and 26K context.
On Intel Arc A770 16GB, Phi 3 Medium 14B can safely use up to 26K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
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.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-3-medium-14b-on-arc-a770-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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