willitrun·ai

Can baichuan inc Baichuan M2 32B run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

C46Usable
Estimated from fit model

baichuan inc Baichuan M2 32B needs ~38.0 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 38.0 GB, 23.8 tok/s, Runs well
38.0 GB required92.2 GB available
41% VRAM used

Fit status

Runs well

Decode

23.8 tok/s

TTFT

8145 ms

Safe context

247K

Memory

38.0 GB / 92.2 GB

Memory breakdown

Weights19.5 GB
KV Cache3.8 GB
Runtime0.9 GB
Headroom13.8 GB

See how fast it feels

See how fast it feelsbaichuan inc Baichuan M2 32B on Mac Studio M2 Ultra 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: 23.8 tok/s decode · 8.1s TTFT (warm) · 59 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
ChatCRuns well23.8 tok/s4442 ms247K
CodingCRuns well23.8 tok/s8145 ms247K
Agentic CodingCRuns well23.8 tok/s11847 ms247K
ReasoningCRuns well23.8 tok/s9625 ms247K
RAGCRuns well23.8 tok/s14808 ms247K

Inference speed

baichuan inc Baichuan M2 32B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for baichuan inc Baichuan M2 32B 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M61.5Tight
MacBook Pro M4 Max 128GB
128 GBQ4_K_M30.8Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M30.8Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M28.5Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M23.8Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M23.2Heavy offload
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M22.5Fits
RX 7900 XTX 24GB
24 GBQ4_K_M21.4Heavy offload
NVIDIARTX 3090 24GB
24 GBQ4_K_M19.8Heavy offload
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M19.4Tight
MacBook Pro M3 Max 64GB
64 GBQ4_K_M12.3Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M11.3Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M8.4Too big
NVIDIARTX 4070 12GB
12 GBQ4_K_M2.9Too big
NVIDIARTX 3060 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 4060 8GB
8 GBQ4_K_M2.0Too 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 inc Baichuan M2 32B (32B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowD40
Q3_K_S
3
15.7 GB
LowD40
NVFP4
4
17.9 GB
MediumC40
Q4_K_M
4
19.5 GB
MediumC40
Q5_K_M
5
23.0 GB
HighC41
Q6_K
6
26.2 GB
HighC41
Q8_0
8
34.2 GB
Very HighC43
F16Best for your GPU
16
65.6 GB
MaximumC47

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

升级选项

能流畅运行 baichuan inc Baichuan M2 32B 的硬件

NVIDIARTX PRO 6000 Blackwell Workstation Edition 96GB经济之选
1792 GB/s (+992)
C
Raises estimated decode speed by about 224%.77.1 tok/s 解码

Raises estimated decode speed by about 224%.

~$9,999 MSRP

NVIDIARTX PRO 6000 Blackwell Server Edition 96GB最佳性价比
1597 GB/s (+797)
C
Raises estimated decode speed by about 189%.68.7 tok/s 解码

Raises estimated decode speed by about 189%.

~$9,999 MSRP

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run baichuan inc Baichuan M2 32B?

Yes, Mac Studio M2 Ultra 128GB can run baichuan inc Baichuan M2 32B with a C grade (Runs well). Expected decode speed: 23.8 tok/s.

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

baichuan inc Baichuan M2 32B (32B parameters) requires approximately 38.0 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 Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, baichuan inc Baichuan M2 32B achieves approximately 23.8 tokens per second decode speed with a time-to-first-token of 8145ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run baichuan inc Baichuan M2 32B for coding?

For coding workloads, baichuan inc Baichuan M2 32B on Mac Studio M2 Ultra 128GB receives a C grade with 23.8 tok/s and 247K context.

What context window can baichuan inc Baichuan M2 32B use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, baichuan inc Baichuan M2 32B can safely use up to 247K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for baichuan inc Baichuan M2 32B?

Not always. Mac Studio M2 Ultra 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.

See all results for Mac Studio M2 Ultra 128GBSee all hardware for baichuan inc Baichuan M2 32B
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