Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for baichuan inc Baichuan M2 32B?

Not always. MacBook Pro M4 Max 64GB 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 baichuan inc Baichuan M2 32B run on MacBook Pro M4 Max 64GB?

YES — Runs Great

C51Usable

Estimated from fit model

baichuan inc Baichuan M2 32B needs ~31.1 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 31.1 GB, 30.8 tok/s, Runs well

31.1 GB required46.1 GB available

67% VRAM used

Fit status

Runs well

Decode

30.8 tok/s

TTFT

6292 ms

Safe context

80K

Memory

31.1 GB / 46.1 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsbaichuan inc Baichuan M2 32B on MacBook Pro M4 Max 64GB
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	C	Runs well	30.8 tok/s	3432 ms	80K
Coding	C	Runs well	17.6 tok/s	10986 ms	80K
Agentic Coding	C	Runs well	30.8 tok/s	9152 ms	80K
Reasoning	C	Runs well	30.8 tok/s	7436 ms	80K
RAG	C	Runs well	30.8 tok/s	11441 ms	80K

Quantization options

How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C44
Q3_K_S	3	15.7 GB	Low	C45
NVFP4	4

Get started

Copy-paste commands to run baichuan inc Baichuan M2 32B on your machine.

Run

lms load hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf && lms server start

Upgrade options

Hardware that runs baichuan inc Baichuan M2 32B well

RTX PRO 5000 Blackwell 48GBBudget pick

1344 GB/s (+798)

Raises estimated decode speed by about 88%.57.8 tok/s decode

Raises estimated decode speed by about 88%.

~$4,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 64GB run baichuan inc Baichuan M2 32B?

Yes, MacBook Pro M4 Max 64GB can run baichuan inc Baichuan M2 32B with a C grade (Runs well). Expected decode speed: 17.6 tok/s.

How much VRAM does baichuan inc Baichuan M2 32B need?

baichuan inc Baichuan M2 32B (32B parameters) requires approximately 31.1 GB of memory with Q4_K_M quantization.

What is the best quantization for baichuan inc Baichuan M2 32B?

The recommended quantization for baichuan inc Baichuan M2 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will baichuan inc Baichuan M2 32B run at on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, baichuan inc Baichuan M2 32B achieves approximately 17.6 tokens per second decode speed with a time-to-first-token of 10986ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 64GB run baichuan inc Baichuan M2 32B for coding?

For coding workloads, baichuan inc Baichuan M2 32B on MacBook Pro M4 Max 64GB receives a C grade with 17.6 tok/s and 80K context.

What context window can baichuan inc Baichuan M2 32B use on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, baichuan inc Baichuan M2 32B can safely use up to 80K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 Max 64GB See all hardware for baichuan inc Baichuan M2 32B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf-on-m4-max-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: