Llama 3.2 11B Vision needs ~10.3 GB but RTX 3050 Ti Laptop 4GB only has 4.0 GB. Try a smaller quantization or lighter model.
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
6.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.6 tok/s
TTFT
53794 ms
Safe context
4K
Memory
10.3 GB / 4.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 10.3 GB, but this setup only exposes 4.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.6 tok/s | 29342 ms | 4K |
| Coding | F | Too heavy | 3.6 tok/s | 53794 ms | 4K |
| Agentic Coding | F | Too heavy | 3.6 tok/s | 78246 ms | 4K |
| Reasoning | F | Too heavy | 3.6 tok/s | 63575 ms | 4K |
| RAG | F | Too heavy | 3.6 tok/s | 97807 ms | 4K |
How Llama 3.2 11B Vision (11B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.3 GB | Low | F0 |
Q3_K_S | 3 | 5.4 GB | Low | F0 |
NVFP4 | 4 | 6.2 GB | Medium | F0 |
Q4_K_M | 4 | 6.7 GB | Medium | F0 |
Q5_K_M | 5 | 7.9 GB | High | F0 |
Q6_K | 6 | 9.0 GB | High | F0 |
Q8_0 | 8 | 11.8 GB | Very High | F0 |
F16 | 16 | 22.5 GB | Maximum | F0 |
Upgrade-Optionen
RTX 3060 12GBGünstige WahlMakes 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.
ca. $329 MSRP
RTX 5060 Ti 16GBBestes Preis-Leistungs-VerhältnisMakes 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.
ca. $449 MSRP
RTX 4060 Ti 16GBNVIDIA-UpgradeMakes 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.
ca. $499 MSRP
No, Llama 3.2 11B Vision requires more memory than RTX 3050 Ti Laptop 4GB provides.
Llama 3.2 11B Vision (11B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3.2 11B Vision is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 Ti Laptop 4GB, Llama 3.2 11B Vision achieves approximately 3.6 tokens per second decode speed with a time-to-first-token of 53794ms using Q4_K_M quantization.
For coding workloads, Llama 3.2 11B Vision on RTX 3050 Ti Laptop 4GB receives a F grade with 3.6 tok/s and 4K context.
On RTX 3050 Ti Laptop 4GB, Llama 3.2 11B Vision can safely use up to 4K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-3.2-11b-vision-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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