Llama 3.2 11B Vision needs ~15.0 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~30 tok/s.
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
Fit status
Runs well
Decode
29.6 tok/s
TTFT
6533 ms
Safe context
16K
Memory
15.0 GB / 34.6 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 29.6 tok/s | 3563 ms | 16K |
| Coding | B | Runs well | 29.6 tok/s | 6533 ms | 16K |
| Agentic Coding | B | Runs well | 29.6 tok/s | 9502 ms | 16K |
| Reasoning | B | Runs well | 29.6 tok/s | 7720 ms | 16K |
| RAG | B | Runs well | 29.6 tok/s | 11877 ms | 16K |
How Llama 3.2 11B Vision (11B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.3 GB | Low | B57 |
Q3_K_S | 3 | 5.4 GB | Low | B58 |
NVFP4 | 4 |
Copy-paste commands to run Llama 3.2 11B Vision on your machine.
Run
ollama run llama3.2-vision:11bUpgrade options
Raises estimated decode speed by about 151%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 138%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 201%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M4 Pro 48GB can run Llama 3.2 11B Vision with a B grade (Runs well). Expected decode speed: 29.6 tok/s.
Llama 3.2 11B Vision (11B parameters) requires approximately 15.0 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 MacBook Pro M4 Pro 48GB, Llama 3.2 11B Vision achieves approximately 29.6 tokens per second decode speed with a time-to-first-token of 6533ms using Q4_K_M quantization.
For coding workloads, Llama 3.2 11B Vision on MacBook Pro M4 Pro 48GB receives a B grade with 29.6 tok/s and 16K context.
On MacBook Pro M4 Pro 48GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-3.2-11b-vision-on-m4-pro-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
6.2 GB |
| Medium |
| B58 |
Q4_K_M | 4 | 6.7 GB | Medium | B58 |
Q5_K_M | 5 | 7.9 GB | High | B59 |
Q6_K | 6 | 9.0 GB | High | B59 |
Q8_0 | 8 | 11.8 GB | Very High | B60 |
F16Best for your GPU | 16 | 22.5 GB | Maximum | B63 |
Not always. MacBook Pro M4 Pro 48GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.