SmolLM3 3B needs ~4.7 GB VRAM. RTX 3050 Ti Laptop 4GB has 4.0 GB. With Q2_K 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
1.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
35.3 tok/s
TTFT
5483 ms
Safe context
5K
Memory
5.4 GB / 4.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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 0.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Very compromised (needs ~0.2 GB host RAM) | 42.0 tok/s | 2514 ms | 5K |
| Coding | F | Too heavy | 32.8 tok/s | 5894 ms | 5K |
| Agentic Coding | F | Too heavy | 18.4 tok/s | 15300 ms | 5K |
| Reasoning | F | Too heavy | 35.3 tok/s | 6479 ms | 5K |
| RAG | F | Too heavy | 18.4 tok/s | 19126 ms | 5K |
How SmolLM3 3B (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 | B63 |
Q3_K_S | 3 | 1.5 GB | Low | B63 |
NVFP4 | 4 | 1.7 GB | Medium | B63 |
Q4_K_MBest for your GPU | 4 | 1.8 GB | Medium | B63 |
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 |
Copy-paste commands to run SmolLM3 3B on your machine.
Run
lms load SmolLM3-3B && lms server startOpções de upgrade
GTX 1660 Super 6GBOpção econômicaMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$229 MSRP
RTX 3050 8GBMelhor custo-benefícioMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$249 MSRP
GTX 1060 6GBUpgrade NVIDIAMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$249 MSRP
Yes, RTX 3050 Ti Laptop 4GB can run SmolLM3 3B at Q2_K quantization (Very compromised (needs ~0.2 GB host RAM)). The recommended Q4_K_M requires 5.4 GB which exceeds available memory, but at Q2_K it needs only 4.7 GB. Expected decode speed: 42.0 tok/s.
SmolLM3 3B (3B parameters) requires approximately 5.4 GB at Q4_K_M quantization. On RTX 3050 Ti Laptop 4GB, it fits at Q2_K using 4.7 GB.
The recommended quantization is Q4_K_M, but on RTX 3050 Ti Laptop 4GB the best fitting quantization is Q2_K, which uses 4.7 GB.
On RTX 3050 Ti Laptop 4GB, SmolLM3 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q2_K quantization.
For coding workloads, SmolLM3 3B on RTX 3050 Ti Laptop 4GB receives a F grade with 32.8 tok/s and 5K context.
On RTX 3050 Ti Laptop 4GB, SmolLM3 3B can safely use up to 10K tokens of context at Q2_K quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/smollm3-3b-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>
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