RX 7900 XTX 24GBOpción económica
960 GB/s (+560)
B
Sube la velocidad estimada de decodificación alrededor de un 214%.110.7 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 214%.
~$999 MSRP
Can Llama 3.2 11B Vision run on MacBook Pro M1 Max 32GB?
YES — Runs Great
B66Good
Estimated from fit model
Llama 3.2 11B Vision needs ~13.3 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~35 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
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, 35.2 tok/s, Runs well
13.3 GB required23.0 GB available
58% VRAM used
Fit status
Runs well
Decode
35.2 tok/s
TTFT
5493 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 M1 Max 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: 35.2 tok/s decode · 5.5s TTFT (warm) · 88 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 | 35.2 tok/s | 2996 ms | 16K |
| Coding | B | Runs well | 35.2 tok/s | 5493 ms | 16K |
| Agentic Coding | B | Runs well | 35.2 tok/s | 7990 ms | 16K |
| Reasoning | B | Runs well | 35.2 tok/s | 6492 ms | 16K |
| RAG | B | Runs well | 35.2 tok/s | 9987 ms | 16K |
Quantization options
How Llama 3.2 11B Vision (11B params) fits at each quantization level on MacBook Pro M1 Max 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 | 6.2 GB | Medium | B61 |
Q4_K_M | 4 | 6.7 GB | Medium | B61 |
Q5_K_M | 5 | 7.9 GB | High | B62 |
Q6_K | 6 | 9.0 GB | High | B63 |
Q8_0Best for your GPU | 8 | 11.8 GB | Very High | B65 |
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:11bOpciones de mejora
Hardware que ejecuta bien Llama 3.2 11B Vision
Frequently asked questions
Can MacBook Pro M1 Max 32GB run Llama 3.2 11B Vision?
Yes, MacBook Pro M1 Max 32GB can run Llama 3.2 11B Vision with a B grade (Runs well). Expected decode speed: 35.2 tok/s.
How much VRAM does Llama 3.2 11B Vision need?
Llama 3.2 11B Vision (11B parameters) requires approximately 13.3 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 M1 Max 32GB?
On MacBook Pro M1 Max 32GB, Llama 3.2 11B Vision achieves approximately 35.2 tokens per second decode speed with a time-to-first-token of 5493ms using Q4_K_M quantization.
Can MacBook Pro M1 Max 32GB run Llama 3.2 11B Vision for coding?
For coding workloads, Llama 3.2 11B Vision on MacBook Pro M1 Max 32GB receives a B grade with 35.2 tok/s and 16K context.
What context window can Llama 3.2 11B Vision use on MacBook Pro M1 Max 32GB?
On MacBook Pro M1 Max 32GB, 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 M1 Max 32GB as fast as VRAM for Llama 3.2 11B Vision?
Not always. MacBook Pro M1 Max 32GB 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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