Llama 3.2 11B Vision needs ~12.5 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~11 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
Fit status
Runs well
Decode
10.9 tok/s
TTFT
17771 ms
Safe context
16K
Memory
12.5 GB / 17.3 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 10.9 tok/s | 9693 ms | 16K |
| Coding | B | Runs well | 10.9 tok/s | 17771 ms | 16K |
| Agentic Coding | B | Tight fit | 10.9 tok/s | 25849 ms | 16K |
| Reasoning | B | Runs well | 10.9 tok/s | 21002 ms | 16K |
| RAG | B | Tight fit | 10.9 tok/s | 32311 ms | 16K |
How Llama 3.2 11B Vision (11B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.3 GB | Low | B62 |
Q3_K_S | 3 | 5.4 GB | Low | B63 |
NVFP4 | 4 | 6.2 GB | Medium | B63 |
Q4_K_M | 4 | 6.7 GB | Medium | B64 |
Q5_K_M | 5 | 7.9 GB | High | B65 |
Q6_K | 6 | 9.0 GB | High | B66 |
Q8_0Best for your GPU | 8 | 11.8 GB | Very High | B65 |
F16 | 16 | 22.5 GB | Maximum | F0 |
Copy-paste commands to run Llama 3.2 11B Vision on your machine.
Run
ollama run llama3.2-vision:11bアップグレードオプション
Raises estimated decode speed by about 241%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Raises estimated decode speed by about 223%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Raises estimated decode speed by about 280%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Yes, MacBook Pro M3 24GB can run Llama 3.2 11B Vision with a B grade (Runs well). Expected decode speed: 10.9 tok/s.
Llama 3.2 11B Vision (11B parameters) requires approximately 12.5 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 M3 24GB, Llama 3.2 11B Vision achieves approximately 10.9 tokens per second decode speed with a time-to-first-token of 17771ms using Q4_K_M quantization.
For coding workloads, Llama 3.2 11B Vision on MacBook Pro M3 24GB receives a B grade with 10.9 tok/s and 16K context.
On MacBook Pro M3 24GB, 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.
Not always. MacBook Pro M3 24GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-3.2-11b-vision-on-m3-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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