RTX 4080 Super 16GBBiggest leap736 GB/s (+536)
A
Raises estimated decode speed by about 345%.97.9 tok/s decode
Raises estimated decode speed by about 345%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
Llama 3.2 11B Vision needs ~11.6 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~21 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
100 MB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0 GB host RAM)
Decode
22.0 tok/s
TTFT
8781 ms
Safe context
15K
Memory
11.6 GB / 11.5 GB
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.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 22.4 tok/s | 4708 ms | 15K |
| Coding | B | Runs with offload | 20.5 tok/s | 9439 ms | 15K |
| Agentic Coding | C | Very compromised (needs ~1 GB host RAM) | 17.5 tok/s | 16067 ms | 15K |
| Reasoning | B | Runs with offload | 20.5 tok/s | 11156 ms | 15K |
| RAG | C | Very compromised (needs ~1 GB host RAM) | 17.5 tok/s | 20084 ms |
How Llama 3.2 11B Vision (11B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.3 GB | Low | B66 |
Q3_K_S | 3 | 5.4 GB | Low | B67 |
NVFP4 | 4 |
Copy-paste commands to run Llama 3.2 11B Vision on your machine.
Run
ollama run llama3.2-vision:11bUpgrade options
RTX 4080 Super 16GBBiggest leapRaises estimated decode speed by about 345%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Yes, MacBook Pro M2 Pro 16GB can run Llama 3.2 11B Vision with a B grade (Runs with offload). Expected decode speed: 20.5 tok/s.
Llama 3.2 11B Vision (11B parameters) requires approximately 11.6 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 M2 Pro 16GB, Llama 3.2 11B Vision achieves approximately 20.5 tokens per second decode speed with a time-to-first-token of 9439ms using Q4_K_M quantization.
For coding workloads, Llama 3.2 11B Vision on MacBook Pro M2 Pro 16GB receives a B grade with 20.5 tok/s and 15K context.
On MacBook Pro M2 Pro 16GB, Llama 3.2 11B Vision can safely use up to 15K 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-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 15K |
6.2 GB |
| Medium |
| B67 |
Q4_K_M | 4 | 6.7 GB | Medium | B66 |
Q5_K_MBest for your GPU | 5 | 7.9 GB | High | B66 |
Q6_K | 6 | 9.0 GB | High | F0 |
Q8_0 | 8 | 11.8 GB | Very High | F0 |
F16 | 16 | 22.5 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M2 Pro 16GB 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.