RTX 3060 12GB经济之选12 GB VRAM (+2)
B
Removes host-memory offload, which is usually the single biggest latency and throughput win.38.1 tok/s 解码
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$329 MSRP
Can Llama 3.2 11B Vision run on RTX 3080 10GB?
BARELY — Tight on Memory
B57Good
Estimated from fit model
Llama 3.2 11B Vision needs ~10.9 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~58 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload
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) — 10.9 GB, 58.3 tok/s, Very compromised (needs ~0.5 GB host RAM)
10.9 GB required10.0 GB available
109% VRAM needed
0.9 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.5 GB host RAM)
Decode
58.3 tok/s
TTFT
3321 ms
Safe context
9K
Memory
10.9 GB / 10.0 GB
Offload
10%
Memory breakdown
Weights6.7 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsLlama 3.2 11B Vision on RTX 3080 10GB
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: 58.3 tok/s decode · 3.3s TTFT (warm) · 146 tok/s prefill
What limits this setup
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% 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.
Best improvement path
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 0.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload | 92.5 tok/s | 1141 ms | 9K |
| Coding | B | Very compromised (needs ~0.5 GB host RAM) | 58.3 tok/s | 3321 ms | 9K |
| Agentic Coding | F | Too heavy | 41.2 tok/s | 6841 ms | 9K |
| Reasoning | B | Very compromised (needs ~0.5 GB host RAM) | 58.3 tok/s | 3924 ms | 9K |
| RAG | F | Too heavy | 41.2 tok/s | 8551 ms | 9K |
Quantization options
How Llama 3.2 11B Vision (11B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.3 GB | Low | B67 |
Q3_K_S | 3 | 5.4 GB | Low | B67 |
NVFP4 | 4 | 6.2 GB | Medium | B67 |
Q4_K_MBest for your GPU | 4 | 6.7 GB | Medium | B67 |
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 |
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 3060 12GB经济之选RTX 5060 Ti 16GB最佳性价比16 GB VRAM (+6)
B
Removes host-memory offload, which is usually the single biggest latency and throughput win.44.5 tok/s 解码
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA 升级16 GB VRAM (+6)
B
Removes host-memory offload, which is usually the single biggest latency and throughput win.33.7 tok/s 解码
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Frequently asked questions
Can RTX 3080 10GB run Llama 3.2 11B Vision?
Yes, RTX 3080 10GB can run Llama 3.2 11B Vision with a B grade (Very compromised (needs ~0.5 GB host RAM)). Expected decode speed: 58.3 tok/s.
How much VRAM does Llama 3.2 11B Vision need?
Llama 3.2 11B Vision (11B parameters) requires approximately 10.9 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 10GB?
On RTX 3080 10GB, Llama 3.2 11B Vision achieves approximately 58.3 tokens per second decode speed with a time-to-first-token of 3321ms using Q4_K_M quantization.
Can RTX 3080 10GB run Llama 3.2 11B Vision for coding?
For coding workloads, Llama 3.2 11B Vision on RTX 3080 10GB receives a B grade with 58.3 tok/s and 9K context.
What context window can Llama 3.2 11B Vision use on RTX 3080 10GB?
On RTX 3080 10GB, Llama 3.2 11B Vision can safely use up to 9K 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 10GB?
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.
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