RTX 5060 Ti 16GB经济之选16 GB VRAM (+4)448 GB/s (+16)
B
Adds memory headroom for longer context windows and future model growth.44.5 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can Llama 3.2 11B Vision run on RTX 4080 Laptop 12GB?
YES — Tight Fit
B67Good
Estimated from fit model
Llama 3.2 11B Vision needs ~11.1 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~54 tok/s.
Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 11.1 GB, 54.0 tok/s, Tight fit
11.1 GB required12.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
54.0 tok/s
TTFT
3586 ms
Safe context
16K
Memory
11.1 GB / 12.0 GB
Memory breakdown
Weights6.7 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsLlama 3.2 11B Vision on RTX 4080 Laptop 12GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 54.0 tok/s decode · 3.6s TTFT (warm) · 135 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 54.0 tok/s | 1956 ms | 16K |
| Coding | B | Tight fit | 54.0 tok/s | 3586 ms | 16K |
| Agentic Coding | B | Very compromised (needs ~0.5 GB host RAM) | 34.1 tok/s | 8253 ms | 16K |
| Reasoning | B | Tight fit | 54.0 tok/s | 4238 ms | 16K |
| RAG | B | Very compromised (needs ~0.5 GB host RAM) | 34.1 tok/s | 10317 ms | 16K |
Quantization options
How Llama 3.2 11B Vision (11B params) fits at each quantization level on RTX 4080 Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.3 GB | Low | B65 |
Q3_K_S | 3 | 5.4 GB | Low | B67 |
NVFP4 | 4 | 6.2 GB | Medium | B67 |
Q4_K_M | 4 | 6.7 GB | Medium | B66 |
Q5_K_M | 5 | 7.9 GB | High | B66 |
Q6_KBest for your GPU | 6 | 9.0 GB | High | B66 |
Q8_0 | 8 | 11.8 GB | Very High | F0 |
F16 | 16 | 22.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Llama 3.2 11B Vision on your machine.
Run
ollama run llama3.2-vision:11b升级选项
能流畅运行 Llama 3.2 11B Vision 的硬件
RTX 5060 Ti 16GB经济之选RTX 4060 Ti 16GB最佳性价比16 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.33.7 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
RTX 5070 Ti 16GBNVIDIA 升级16 GB VRAM (+4)896 GB/s (+464)
A
Raises estimated decode speed by about 70%.91.9 tok/s 解码
Raises estimated decode speed by about 70%.
Adds memory headroom for longer context windows and future model growth.
~$749 MSRP
Frequently asked questions
Can RTX 4080 Laptop 12GB run Llama 3.2 11B Vision?
Yes, RTX 4080 Laptop 12GB can run Llama 3.2 11B Vision with a B grade (Tight fit). Expected decode speed: 54.0 tok/s.
How much VRAM does Llama 3.2 11B Vision need?
Llama 3.2 11B Vision (11B parameters) requires approximately 11.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Llama 3.2 11B Vision?
The recommended quantization for Llama 3.2 11B Vision is Q4_K_M, which balances quality and memory efficiency.
What speed will Llama 3.2 11B Vision run at on RTX 4080 Laptop 12GB?
On RTX 4080 Laptop 12GB, Llama 3.2 11B Vision achieves approximately 54.0 tokens per second decode speed with a time-to-first-token of 3586ms using Q4_K_M quantization.
Can RTX 4080 Laptop 12GB run Llama 3.2 11B Vision for coding?
For coding workloads, Llama 3.2 11B Vision on RTX 4080 Laptop 12GB receives a B grade with 54.0 tok/s and 16K context.
What context window can Llama 3.2 11B Vision use on RTX 4080 Laptop 12GB?
On RTX 4080 Laptop 12GB, Llama 3.2 11B Vision can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
What should I upgrade first if Llama 3.2 11B Vision feels slow on RTX 4080 Laptop 12GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/llama-3.2-11b-vision-on-rtx-4080-laptop-12gb" 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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