Is unified memory on MacBook Pro M3 Max 128GB as fast as VRAM for Llama 3.3 70B Instruct?

Not always. MacBook Pro M3 Max 128GB 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.

Can Llama 3.3 70B Instruct run on MacBook Pro M3 Max 128GB?

Q: What should I upgrade first if Llama 3.3 70B Instruct feels slow on MacBook Pro M3 Max 128GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — Runs Great

C48Usable

Estimated from fit model

Llama 3.3 70B Instruct needs ~65.6 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~6 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 65.6 GB, 5.6 tok/s, Runs well

65.6 GB required92.2 GB available

71% VRAM used

Fit status

Runs well

Decode

5.6 tok/s

TTFT

34445 ms

Safe context

68K

Memory

65.6 GB / 92.2 GB

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B Instruct on MacBook Pro M3 Max 128GB
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: 5.6 tok/s decode · 34.4s TTFT (warm) · 14 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	5.6 tok/s	18788 ms	68K
Coding	C	Runs well	5.6 tok/s	34445 ms	68K
Agentic Coding	C	Runs well	5.6 tok/s	50101 ms	68K
Reasoning	C	Runs well	5.6 tok/s	40707 ms	68K
RAG	C	Runs well	5.6 tok/s	62627 ms	68K

Quantization options

How Llama 3.3 70B Instruct (70B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	C43
Q3_K_S	3	34.3 GB	Low	C44
NVFP4	4

Get started

Copy-paste commands to run Llama 3.3 70B Instruct on your machine.

Run

lms load hf-maziyarpanahi--llama-3-3-70b-instruct-gguf && lms server start

Upgrade options

Hardware that runs Llama 3.3 70B Instruct well

RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

1792 GB/s (+1392)

Raises estimated decode speed by about 530%.35.3 tok/s decode

Raises estimated decode speed by about 530%.

Moves the workload away from shared memory into dedicated accelerator memory.

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBBest value

1597 GB/s (+1197)

Raises estimated decode speed by about 461%.31.4 tok/s decode

Raises estimated decode speed by about 461%.

Moves the workload away from shared memory into dedicated accelerator memory.

~$9,999 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 128GB run Llama 3.3 70B Instruct?

Yes, MacBook Pro M3 Max 128GB can run Llama 3.3 70B Instruct with a C grade (Runs well). Expected decode speed: 5.6 tok/s.

How much VRAM does Llama 3.3 70B Instruct need?

Llama 3.3 70B Instruct (70B parameters) requires approximately 65.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B Instruct?

The recommended quantization for Llama 3.3 70B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.3 70B Instruct run at on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, Llama 3.3 70B Instruct achieves approximately 5.6 tokens per second decode speed with a time-to-first-token of 34445ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 128GB run Llama 3.3 70B Instruct for coding?

For coding workloads, Llama 3.3 70B Instruct on MacBook Pro M3 Max 128GB receives a C grade with 5.6 tok/s and 68K context.

What context window can Llama 3.3 70B Instruct use on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, Llama 3.3 70B Instruct can safely use up to 68K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M3 Max 128GB See all hardware for Llama 3.3 70B Instruct

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