MacBook Pro M3 Pro 36GBBudget pick
36 GB Unified (+4)150 GB/s (+30)
B
Raises estimated decode speed by about 43%.17.5 tok/s decode
Raises estimated decode speed by about 43%.
~$1,999 MSRP
Can Llama 3.2 11B Vision run on MacBook Pro M4 32GB?
YES — Runs Great
B62Good
○Estimated from fit model
Llama 3.2 11B Vision needs ~13.3 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~13 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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) — 13.3 GB, 12.2 tok/s, Runs well
13.3 GB required23.0 GB available
58% VRAM used
Fit status
Runs well
Decode
12.2 tok/s
TTFT
15894 ms
Safe context
16K
Memory
13.3 GB / 23.0 GB
Memory breakdown
Weights6.7 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsLlama 3.2 11B Vision on MacBook Pro M4 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: 12.2 tok/s decode · 15.9s TTFT (warm) · 31 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 12.2 tok/s | 8669 ms | 16K |
| Coding | B | Runs well | 12.9 tok/s | 15035 ms | 16K |
| Agentic Coding | B | Runs well | 12.2 tok/s | 23118 ms | 16K |
| Reasoning | B | Runs well | 12.2 tok/s | 18783 ms | 16K |
| RAG | B | Runs well | 12.2 tok/s | 28898 ms | 16K |
Quantization options
How Llama 3.2 11B Vision (11B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.3 GB | Low | B60 |
Q3_K_S | 3 | 5.4 GB | Low | B60 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Llama 3.2 11B Vision on your machine.
Run
ollama run llama3.2-vision:11bUpgrade options
Hardware that runs Llama 3.2 11B Vision well
MacBook Pro M4 Max 36GBBest value
36 GB Unified (+4)410 GB/s (+290)
B
Raises estimated decode speed by about 239%.41.4 tok/s decode
Raises estimated decode speed by about 239%.
~$2,499 MSRP
MacBook Pro M4 Max 48GBApple upgrade
48 GB Unified (+16)546 GB/s (+426)
B
Raises estimated decode speed by about 298%.48.5 tok/s decode
Raises estimated decode speed by about 298%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP