RTX 5060 Ti 16GBOpção econômica16 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.44.5 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can Llama 3.2 11B Vision run on RTX 3080 Ti 12GB?
YES — Tight Fit
B68Good
Estimated from fit model
Llama 3.2 11B Vision needs ~11.1 GB VRAM. RTX 3080 Ti 12GB has 12.0 GB. With Q4_K_M quantization, expect ~101 tok/s.
Runtime: OllamaCapacity: TightBandwidth: HighStack: 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, 108.1 tok/s, Tight fit
11.1 GB required12.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
108.1 tok/s
TTFT
1791 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 3080 Ti 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: 108.1 tok/s decode · 1.8s TTFT (warm) · 270 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 | 108.1 tok/s | 977 ms | 16K |
| Coding | B | Tight fit | 100.6 tok/s | 1925 ms | 16K |
| Agentic Coding | B | Very compromised (needs ~0.5 GB host RAM) | 68.3 tok/s | 4121 ms | 16K |
| Reasoning | B | Tight fit | 108.1 tok/s | 2116 ms | 16K |
| RAG | B | Very compromised (needs ~0.5 GB host RAM) | 68.3 tok/s | 5151 ms | 16K |
Quantization options
How Llama 3.2 11B Vision (11B params) fits at each quantization level on RTX 3080 Ti 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:11bOpções de upgrade
Hardware que roda bem Llama 3.2 11B Vision
RTX 5060 Ti 16GBOpção econômicaRTX 5070 Ti 16GBMelhor custo-benefício16 GB VRAM (+4)
A
Adds memory headroom for longer context windows and future model growth.91.9 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$749 MSRP
RTX 4070 Ti Super 16GBUpgrade NVIDIA16 GB VRAM (+4)
A
Adds memory headroom for longer context windows and future model growth.86.1 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Frequently asked questions
Can RTX 3080 Ti 12GB run Llama 3.2 11B Vision?
Yes, RTX 3080 Ti 12GB can run Llama 3.2 11B Vision with a B grade (Tight fit). Expected decode speed: 100.6 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 3080 Ti 12GB?
On RTX 3080 Ti 12GB, Llama 3.2 11B Vision achieves approximately 100.6 tokens per second decode speed with a time-to-first-token of 1925ms using Q4_K_M quantization.
Can RTX 3080 Ti 12GB run Llama 3.2 11B Vision for coding?
For coding workloads, Llama 3.2 11B Vision on RTX 3080 Ti 12GB receives a B grade with 100.6 tok/s and 16K context.
What context window can Llama 3.2 11B Vision use on RTX 3080 Ti 12GB?
On RTX 3080 Ti 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 3080 Ti 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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