Qwen2.5 1.5B Instruct needs ~2.6 GB VRAM. Intel Arc Pro A40 6GB has 6.0 GB. With Q4_K_M quantization, expect ~21 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
Runs well
Decode
21.0 tok/s
TTFT
9219 ms
Safe context
326K
Memory
2.6 GB / 6.0 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 | C | Runs well | 21.0 tok/s | 5029 ms | 287K |
| Coding | C | Runs well | 21.0 tok/s | 9219 ms | 326K |
| Agentic Coding | C | Runs well | 21.0 tok/s | 13410 ms | 326K |
| Reasoning | C | Runs well | 21.0 tok/s | 10895 ms | 326K |
| RAG | C | Runs well | 21.0 tok/s | 16762 ms | 326K |
How Qwen2.5 1.5B Instruct (1.5B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | C52 |
Q3_K_S | 3 | 0.7 GB | Low | C52 |
NVFP4 | 4 |
Copy-paste commands to run Qwen2.5 1.5B Instruct on your machine.
Run
lms load hf-qwen--qwen2-5-1-5b-instruct-gguf && lms server startYes, Intel Arc Pro A40 6GB can run Qwen2.5 1.5B Instruct with a C grade (Runs well). Expected decode speed: 21.0 tok/s.
Qwen2.5 1.5B Instruct (1.5B parameters) requires approximately 2.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen2.5 1.5B Instruct is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro A40 6GB, Qwen2.5 1.5B Instruct achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.
For coding workloads, Qwen2.5 1.5B Instruct on Intel Arc Pro A40 6GB receives a C grade with 21.0 tok/s and 326K context.
On Intel Arc Pro A40 6GB, Qwen2.5 1.5B Instruct can safely use up to 326K tokens of context. The model's official context limit is —, 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/hf-qwen--qwen2-5-1-5b-instruct-gguf-on-arc-pro-a40-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
0.8 GB |
| Medium |
| C52 |
Q4_K_M | 4 | 0.9 GB | Medium | C53 |
Q5_K_M | 5 | 1.1 GB | High | C53 |
Q6_K | 6 | 1.2 GB | High | C53 |
Q8_0 | 8 | 1.6 GB | Very High | C54 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | C54 |
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.