RTX 4080 Super 16GBMaior salto736 GB/s (+586)
A
Raises estimated decode speed by about 459%.97.9 tok/s decodificação
Raises estimated decode speed by about 459%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
Can Llama 3.2 11B Vision run on MacBook Pro M3 Pro 18GB?
YES — Tight Fit
B63Good
Estimated from fit model
Llama 3.2 11B Vision needs ~11.8 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~18 tok/s.
Runtime: OllamaCapacity: TightBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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) — 11.8 GB, 17.5 tok/s, Tight fit
11.8 GB required13.0 GB available
91% VRAM used
Fit status
Tight fit
Decode
17.5 tok/s
TTFT
11036 ms
Safe context
16K
Memory
11.8 GB / 13.0 GB
Memory breakdown
Weights6.7 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom1.9 GB
See how fast it feels
See how fast it feelsLlama 3.2 11B Vision on MacBook Pro M3 Pro 18GB
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: 17.5 tok/s decode · 11.0s TTFT (warm) · 44 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 | Tight fit | 17.5 tok/s | 6020 ms | 16K |
| Coding | B | Tight fit | 17.5 tok/s | 11036 ms | 16K |
| Agentic Coding | C | Runs with offload (needs ~0.4 GB host RAM) | 15.7 tok/s | 17890 ms | 16K |
| Reasoning | B | Tight fit | 17.5 tok/s | 13042 ms | 16K |
| RAG | C | Runs with offload (needs ~0.4 GB host RAM) | 15.7 tok/s | 22362 ms | 16K |
Quantization options
How Llama 3.2 11B Vision (11B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.3 GB | Low | B65 |
Q3_K_S | 3 | 5.4 GB | Low | B66 |
NVFP4 | 4 | 6.2 GB | Medium | B67 |
Q4_K_M | 4 | 6.7 GB | Medium | B66 |
Q5_K_M | 5 | 7.9 GB | High | B66 |
Q6_KBest for your GPU | 6 | 9.0 GB | High | B66 |
Q8_0 | 8 | 11.8 GB | Very High | F0 |
F16 | 16 | 22.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Llama 3.2 11B Vision on your machine.
Run
ollama run llama3.2-vision:11bOpções de upgrade
Hardware que roda bem Llama 3.2 11B Vision
RTX 4080 Super 16GBMaior saltoMacBook Air M4 24GBOpção econômica
24 GB Unified (+6)
B
Adds memory headroom for longer context windows and future model growth.12.2 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
MacBook Pro M3 24GBMelhor custo-benefício
24 GB Unified (+6)
B
Adds memory headroom for longer context windows and future model growth.10.9 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
MacBook Air M3 24GBUpgrade Apple
24 GB Unified (+6)
B
Adds memory headroom for longer context windows and future model growth.10.9 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Frequently asked questions
Can MacBook Pro M3 Pro 18GB run Llama 3.2 11B Vision?
Yes, MacBook Pro M3 Pro 18GB can run Llama 3.2 11B Vision with a B grade (Tight fit). Expected decode speed: 17.5 tok/s.
How much VRAM does Llama 3.2 11B Vision need?
Llama 3.2 11B Vision (11B parameters) requires approximately 11.8 GB of memory with Q4_K_M quantization.
What is the best quantization for Llama 3.2 11B Vision?
The recommended quantization for Llama 3.2 11B Vision is Q4_K_M, which balances quality and memory efficiency.
What speed will Llama 3.2 11B Vision run at on MacBook Pro M3 Pro 18GB?
On MacBook Pro M3 Pro 18GB, Llama 3.2 11B Vision achieves approximately 17.5 tokens per second decode speed with a time-to-first-token of 11036ms using Q4_K_M quantization.
Can MacBook Pro M3 Pro 18GB run Llama 3.2 11B Vision for coding?
For coding workloads, Llama 3.2 11B Vision on MacBook Pro M3 Pro 18GB receives a B grade with 17.5 tok/s and 16K context.
What context window can Llama 3.2 11B Vision use on MacBook Pro M3 Pro 18GB?
On MacBook Pro M3 Pro 18GB, 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.
Is unified memory on MacBook Pro M3 Pro 18GB as fast as VRAM for Llama 3.2 11B Vision?
Not always. MacBook Pro M3 Pro 18GB 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.
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