1555 GB/s (+1009)
B
Raises estimated decode speed by about 218%.154 tok/s decode
Raises estimated decode speed by about 218%.
~$10,000 MSRP
Can Llama 3.2 11B Vision run on MacBook Pro M4 Max 48GB?
YES — Runs Great
B63Good
○Estimated from fit model
Llama 3.2 11B Vision needs ~15.0 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~49 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) — 15.0 GB, 48.5 tok/s, Runs well
15.0 GB required34.6 GB available
43% VRAM used
Fit status
Runs well
Decode
48.5 tok/s
TTFT
3992 ms
Safe context
16K
Memory
15.0 GB / 34.6 GB
Memory breakdown
Weights6.7 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom5.2 GB
See how fast it feels
See how fast it feelsLlama 3.2 11B Vision on MacBook Pro M4 Max 48GB
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: 48.5 tok/s decode · 4.0s TTFT (warm) · 121 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 | 48.5 tok/s | 2178 ms | 16K |
| Coding | B | Runs well | 48.5 tok/s | 3992 ms | 16K |
| Agentic Coding | B | Runs well | 48.5 tok/s | 5807 ms | 16K |
| Reasoning | B | Runs well | 48.5 tok/s | 4718 ms | 16K |
| RAG | B | Runs well | 48.5 tok/s | 7258 ms | 16K |
Quantization options
How Llama 3.2 11B Vision (11B params) fits at each quantization level on MacBook Pro M4 Max 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 | 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 |
Get started
Copy-paste commands to run Llama 3.2 11B Vision on your machine.
Run
ollama run llama3.2-vision:11bUpgrade options