GTX 1660 Super 6GBBudget pick6 GB VRAM (+2)336 GB/s (+144)
C
Adds memory headroom for longer context windows and future model growth.42 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$229 MSRP
Qwen2.5 3B Instruct needs ~3.8 GB VRAM. RTX 3050 Ti Laptop 4GB has 4.0 GB. With Q4_K_M quantization, expect ~42 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
42.0 tok/s
TTFT
4610 ms
Safe context
26K
Memory
3.8 GB / 4.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 42.0 tok/s | 2514 ms | 26K |
| Coding | C | Tight fit | 42.0 tok/s | 4610 ms | 26K |
| Agentic Coding | C | Runs with offload | 42.0 tok/s | 6705 ms | 26K |
| Reasoning | C | Tight fit | 42.0 tok/s | 5448 ms | 26K |
| RAG | C | Runs with offload | 42.0 tok/s | 8381 ms | 26K |
How Qwen2.5 3B Instruct (3B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | B56 |
Q3_K_S | 3 | 1.5 GB | Low | B55 |
NVFP4 | 4 |
Copy-paste commands to run Qwen2.5 3B Instruct on your machine.
Run
lms load hf-qwen--qwen2-5-3b-instruct-gguf && lms server startUpgrade options
GTX 1660 Super 6GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$229 MSRP
GTX 1060 6GBBest valueAdds memory headroom for longer context windows and future model growth.
~$249 MSRP
GTX 1660 Ti 6GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$279 MSRP
Yes, RTX 3050 Ti Laptop 4GB can run Qwen2.5 3B Instruct with a C grade (Tight fit). Expected decode speed: 42.0 tok/s.
Qwen2.5 3B Instruct (3B parameters) requires approximately 3.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen2.5 3B Instruct is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 Ti Laptop 4GB, Qwen2.5 3B Instruct achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
For coding workloads, Qwen2.5 3B Instruct on RTX 3050 Ti Laptop 4GB receives a C grade with 42.0 tok/s and 26K context.
On RTX 3050 Ti Laptop 4GB, Qwen2.5 3B Instruct can safely use up to 26K 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--qwen2-5-3b-instruct-gguf-on-rtx-3050-ti-laptop-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| B55 |
Q4_K_MBest for your GPU | 4 | 1.8 GB | Medium | B55 |
Q5_K_M | 5 | 2.2 GB | High | F0 |
Q6_K | 6 | 2.5 GB | High | F0 |
Q8_0 | 8 | 3.2 GB | Very High | F0 |
F16 | 16 | 6.1 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.