Qwen 2.5 14B needs ~14.0 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
31.9 tok/s
TTFT
6075 ms
Safe context
27K
Memory
14.0 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 | A | Runs well | 31.9 tok/s | 3314 ms | 27K |
| Coding | A | Tight fit | 31.9 tok/s | 6075 ms | 27K |
| Agentic Coding | B | Runs with offload (needs ~0.5 GB host RAM) | 21.3 tok/s | 13219 ms | 27K |
| Reasoning | A | Tight fit | 31.9 tok/s | 7179 ms | 27K |
| RAG | B | Runs with offload (needs ~0.5 GB host RAM) | 21.3 tok/s | 16524 ms |
How Qwen 2.5 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 | A80 |
Q3_K_S | 3 | 6.9 GB | Low | A82 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 2.5 14B on your machine.
Run
ollama run qwen2.5Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Phi-4-reasoning-plus 14B | 14.7B | S | 30.2 tok/s | |
 GPT-OSS 20B | 21B | A | 29.2 tok/s |
Yes, Intel Arc A770 16GB can run Qwen 2.5 14B with a A grade (Tight fit). Expected decode speed: 31.9 tok/s.
Qwen 2.5 14B (14B parameters) requires approximately 14.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 2.5 14B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A770 16GB, Qwen 2.5 14B achieves approximately 31.9 tokens per second decode speed with a time-to-first-token of 6075ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 14B on Intel Arc A770 16GB receives a A grade with 31.9 tok/s and 27K context.
On Intel Arc A770 16GB, Qwen 2.5 14B can safely use up to 27K tokens of context. The model's official context limit is 131K, 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/qwen-2.5-14b-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>
Preview:
| 27K |
| Medium |
| A82 |
Q4_K_M | 4 | 8.5 GB | Medium | A82 |
Q5_K_M | 5 | 10.1 GB | High | A82 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | A82 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
 Codestral 2 25.08 | 22B | A | 10.7 tok/s |
 CogVLM2 19B | 19B | A | 16.4 tok/s |
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.