40 GB VRAM (+8)1555 GB/s (+915)
B
Raises estimated decode speed by about 155%.154 tok/s decode
Raises estimated decode speed by about 155%.
Adds memory headroom for longer context windows and future model growth.
~$10,000 MSRP
Can Llama 3.2 11B Vision run on Radeon AI PRO R9700 32GB?
YES — Runs Great
B64Good
○Estimated from fit model
Llama 3.2 11B Vision needs ~13.1 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~61 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Share:
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) — 13.1 GB, 60.5 tok/s, Runs well
13.1 GB required32.0 GB available
41% VRAM used
Fit status
Runs well
Decode
60.5 tok/s
TTFT
3200 ms
Safe context
16K
Memory
13.1 GB / 32.0 GB
Memory breakdown
Weights6.7 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsLlama 3.2 11B Vision on Radeon AI PRO R9700 32GB
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: 60.5 tok/s decode · 3.2s TTFT (warm) · 151 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 60.5 tok/s | 1746 ms | 16K |
| Coding | B | Runs well | 60.5 tok/s | 3200 ms | 16K |
| Agentic Coding | B | Runs well | 60.5 tok/s | 4655 ms | 16K |
| Reasoning | B | Runs well | 60.5 tok/s | 3782 ms | 16K |
| RAG | B | Runs well | 60.5 tok/s | 5819 ms | 16K |
Quantization options
How Llama 3.2 11B Vision (11B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.3 GB | Low | B58 |
Q3_K_S | 3 | 5.4 GB | Low | B58 |
NVFP4 | 4 | 6.2 GB | Medium | B58 |
Q4_K_M | 4 | 6.7 GB | Medium | B59 |
Q5_K_M | 5 | 7.9 GB | High | B59 |
Q6_K | 6 | 9.0 GB | High | B60 |
Q8_0 | 8 | 11.8 GB | Very High | B61 |
F16Best for your GPU | 16 | 22.5 GB | Maximum | B63 |
Get started
Copy-paste commands to run Llama 3.2 11B Vision on your machine.
Run
ollama run llama3.2-vision:11bUpgrade options