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URL: https://willitrunai.com/can-run/llama-3.2-11b-vision-on-m3-24gb

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Can Llama 3.2 11B Vision run on MacBook Pro M3 24GB?

YES — Runs Great

B64Good
○Estimated from fit model

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.

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.5 GB, 10.9 tok/s, Runs well
12.5 GB required17.3 GB available
72% VRAM used

Fit status

Runs well

Decode

10.9 tok/s

TTFT

17771 ms

Safe context

16K

Memory

12.5 GB / 17.3 GB

Memory breakdown

Weights6.7 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom2.6 GB

See how fast it feels

See how fast it feelsLlama 3.2 11B Vision on MacBook Pro M3 24GB
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: 10.9 tok/s decode · 17.8s TTFT (warm) · 27 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well10.9 tok/s9693 ms16K
CodingBRuns well10.9 tok/s17771 ms16K
Agentic CodingBTight fit10.9 tok/s25849 ms16K
ReasoningBRuns well10.9 tok/s21002 ms16K
RAGBTight fit10.9 tok/s32311 ms16K

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.3 GB
LowB62
Q3_K_S
3
5.4 GB
LowB63
NVFP4
4

Get started

Copy-paste commands to run Llama 3.2 11B Vision on your machine.

Run

ollama run llama3.2-vision:11b

Upgrade options

Hardware that runs Llama 3.2 11B Vision well

MacBook Pro M2 Max 32GBBudget pick
32 GB Unified (+8)400 GB/s (+300)
B
Raises estimated decode speed by about 241%.37.2 tok/s decode

Raises estimated decode speed by about 241%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

MacBook Pro M1 Max 32GBBest value
32 GB Unified (+8)400 GB/s (+300)
B
Raises estimated decode speed by about 223%.35.2 tok/s decode

Raises estimated decode speed by about 223%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

MacBook Pro M4 Max 36GBApple upgrade
36 GB Unified (+12)410 GB/s (+310)
B
Raises estimated decode speed by about 280%.41.4 tok/s decode

Raises estimated decode speed by about 280%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

Frequently asked questions

See all results for MacBook Pro M3 24GBSee all hardware for Llama 3.2 11B Vision
6.2 GB
Medium
B63
Q4_K_M
4
6.7 GB
MediumB64
Q5_K_M
5
7.9 GB
HighB65
Q6_K
6
9.0 GB
HighB66
Q8_0Best for your GPU
8
11.8 GB
Very HighB65
F16
16
22.5 GB
MaximumF0

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.