Qwen 2.5 14B needs ~13.6 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~17 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
1.6 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1 GB host RAM)
Decode
18.9 tok/s
TTFT
10266 ms
Safe context
7K
Memory
13.6 GB / 12.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% 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.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload | 21.2 tok/s | 4993 ms | 7K |
| Coding | B | Very compromised | 16.6 tok/s | 11642 ms | 7K |
| Agentic Coding | F | Too heavy | 11.0 tok/s | 25554 ms | 7K |
| Reasoning | B | Very compromised | 16.6 tok/s | 13759 ms | 7K |
| RAG | F | Too heavy | 11.0 tok/s | 31943 ms | 7K |
How Qwen 2.5 14B (14B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A83 |
Q3_K_S | 3 | 6.9 GB | Low | A83 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 2.5 14B on your machine.
Run
ollama run qwen2.5Upgrade options
RTX 5060 Ti 16GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 80%.
~$449 MSRP
RTX 4060 Ti 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 48%.
~$499 MSRP
RTX 5070 Ti 16GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 272%.
~$749 MSRP
Yes, RTX 3500 Ada Laptop 12GB can run Qwen 2.5 14B with a B grade (Very compromised). Expected decode speed: 16.6 tok/s.
Qwen 2.5 14B (14B parameters) requires approximately 13.6 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 RTX 3500 Ada Laptop 12GB, Qwen 2.5 14B achieves approximately 16.6 tokens per second decode speed with a time-to-first-token of 11642ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 14B on RTX 3500 Ada Laptop 12GB receives a B grade with 16.6 tok/s and 7K context.
On RTX 3500 Ada Laptop 12GB, Qwen 2.5 14B can safely use up to 7K 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-rtx-3500-ada-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
7.8 GB |
| Medium |
| A83 |
Q4_K_MBest for your GPU | 4 | 8.5 GB | Medium | A82 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 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.