Can Llama 3.1 8B run on MacBook Pro M2 Pro 16GB?

Q: Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Llama 3.1 8B?

Not always. MacBook Pro M2 Pro 16GB 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.

YES — Tight Fit

A71Great

Estimated from fit model

Llama 3.1 8B needs ~9.5 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.5 GB, 30.8 tok/s, Tight fit

9.5 GB required11.5 GB available

83% VRAM used

Fit status

Tight fit

Decode

30.8 tok/s

TTFT

6278 ms

Safe context

33K

Memory

9.5 GB / 11.5 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsLlama 3.1 8B on MacBook Pro M2 Pro 16GB
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: 30.8 tok/s decode · 6.3s TTFT (warm) · 77 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	A	Runs well	30.8 tok/s	3424 ms	33K
Coding	A	Tight fit	30.8 tok/s	6278 ms	33K
Agentic Coding	A	Runs with offload	28.7 tok/s	9816 ms	33K
Reasoning	A	Tight fit	30.8 tok/s	7419 ms	33K
RAG	A	Runs with offload	30.8 tok/s	11414 ms	33K

Quantization options

How Llama 3.1 8B (8B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A71
Q3_K_S	3	3.9 GB	Low	A72
NVFP4	4

Get started

Copy-paste commands to run Llama 3.1 8B on your machine.

Run

ollama run llama3.1

Your hardware

More models your MacBook Pro M2 Pro 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	27.4 tok/s
Qwen 3 14B	14B	A	13.8 tok/s

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run Llama 3.1 8B?

Yes, MacBook Pro M2 Pro 16GB can run Llama 3.1 8B with a A grade (Tight fit). Expected decode speed: 30.8 tok/s.

How much VRAM does Llama 3.1 8B need?

Llama 3.1 8B (8B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.1 8B?

The recommended quantization for Llama 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.1 8B run at on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Llama 3.1 8B achieves approximately 30.8 tokens per second decode speed with a time-to-first-token of 6278ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 16GB run Llama 3.1 8B for coding?

For coding workloads, Llama 3.1 8B on MacBook Pro M2 Pro 16GB receives a A grade with 30.8 tok/s and 33K context.

What context window can Llama 3.1 8B use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Llama 3.1 8B can safely use up to 33K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Llama 3.1 8B?

See all results for MacBook Pro M2 Pro 16GB See all hardware for Llama 3.1 8B

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