Can Baichuan M2 32B Q4 K M run on Mac mini M4 64GB?

YES — Runs Great

C48Usable

Estimated — low-sample bucket· few comparable runs

Baichuan M2 32B Q4 K M needs ~31.1 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 31.1 GB, 8.0 tok/s, Runs well

31.1 GB required46.1 GB available

67% VRAM used

Fit status

Runs well

Decode

8.0 tok/s

TTFT

24300 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 M2 32B Q4 K M on Mac mini M4 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: 8.0 tok/s decode · 24.3s TTFT (warm) · 20 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	8.0 tok/s	13254 ms	80K
Coding	C	Runs well	8.0 tok/s	24300 ms	80K
Agentic Coding	C	Runs well	8.0 tok/s	35345 ms	80K
Reasoning	C	Runs well	8.0 tok/s	28718 ms	80K
RAG	C	Runs well	8.0 tok/s	44181 ms	80K

Inference speed

Baichuan M2 32B Q4 K M inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Baichuan M2 32B Q4 K M at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~62 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	61.5	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	30.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	30.8	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	28.5	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	23.8	Fits
RTX 4090 24GB	24 GB	Q4_K_M	23.2	Heavy offload
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	22.5	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	21.4	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	19.8	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	19.4	Tight
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	12.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	11.3	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	8.4	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.9	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on Mac mini M4 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	17.9 GB	Medium	C46
Q4_K_M	4	19.5 GB	Medium	C46
Q5_K_M	5	23.0 GB	High	C47
Q6_K	6	26.2 GB	High	C48
Q8_0Best for your GPU	8	34.2 GB	Very High	C47
F16	16	65.6 GB	Maximum	F0

Get started

Copy-paste commands to run Baichuan M2 32B Q4 K M on your machine.

Run

lms load hf-baichuan-inc--baichuan-m2-32b-q4-k-m-gguf && lms server start

アップグレードオプション

Baichuan M2 32B Q4 K Mを快適に動かすハードウェア

MacBook Pro M4 Max 96GBコスパ重視

96 GB Unified (+32)546 GB/s (+426)

Raises estimated decode speed by about 285%.30.8 tok/sデコード

Raises estimated decode speed by about 285%.

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

〜$2,499 MSRP

Mac Studio M3 Ultra 96GBコスパ最良

96 GB Unified (+32)819 GB/s (+699)

Raises estimated decode speed by about 256%.28.5 tok/sデコード

Raises estimated decode speed by about 256%.

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

〜$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run Baichuan M2 32B Q4 K M?

Yes, Mac mini M4 64GB can run Baichuan M2 32B Q4 K M with a C grade (Runs well). Expected decode speed: 8.0 tok/s.

How much VRAM does Baichuan M2 32B Q4 K M need?

Baichuan M2 32B Q4 K M (32B parameters) requires approximately 31.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Baichuan M2 32B Q4 K M?

The recommended quantization for Baichuan M2 32B Q4 K M is Q4_K_M, which balances quality and memory efficiency.

What speed will Baichuan M2 32B Q4 K M run at on Mac mini M4 64GB?

On Mac mini M4 64GB, Baichuan M2 32B Q4 K M achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24300ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Baichuan M2 32B Q4 K M for coding?

For coding workloads, Baichuan M2 32B Q4 K M on Mac mini M4 64GB receives a C grade with 8.0 tok/s and 80K context.

What context window can Baichuan M2 32B Q4 K M use on Mac mini M4 64GB?

On Mac mini M4 64GB, Baichuan M2 32B Q4 K M can safely use up to 80K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for Baichuan M2 32B Q4 K M?

Not always. Mac mini M4 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.

See all results for Mac mini M4 64GB See all hardware for Baichuan M2 32B Q4 K M

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