Can baichuan inc Baichuan M2 32B run on RTX PRO 6000 Blackwell Server Edition 96GB?

YES — Runs Great

C48Usable

Estimated from fit model

baichuan inc Baichuan M2 32B needs ~34.1 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~69 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 34.1 GB, 68.7 tok/s, Runs well

34.1 GB required96.0 GB available

36% VRAM used

Fit status

Runs well

Decode

68.7 tok/s

TTFT

2817 ms

Safe context

280K

Memory

34.1 GB / 96.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsbaichuan inc Baichuan M2 32B on RTX PRO 6000 Blackwell Server Edition 96GB
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: 68.7 tok/s decode · 2.8s TTFT (warm) · 172 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	68.7 tok/s	1537 ms	280K
Coding	C	Runs well	68.7 tok/s	2817 ms	280K
Agentic Coding	C	Runs well	68.7 tok/s	4098 ms	280K
Reasoning	C	Runs well	68.7 tok/s	3329 ms	280K
RAG	C	Runs well	68.7 tok/s	5122 ms	280K

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 / 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 inc Baichuan M2 32B (32B params) fits at each quantization level on RTX PRO 6000 Blackwell Server Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	D39
Q3_K_S	3	15.7 GB	Low	D40
NVFP4	4	17.9 GB	Medium	D40
Q4_K_M	4	19.5 GB	Medium	C40
Q5_K_M	5	23.0 GB	High	C41
Q6_K	6	26.2 GB	High	C41
Q8_0	8	34.2 GB	Very High	C43
F16Best for your GPU	16	65.6 GB	Maximum	C47

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

Frequently asked questions

Can RTX PRO 6000 Blackwell Server Edition 96GB run baichuan inc Baichuan M2 32B?

Yes, RTX PRO 6000 Blackwell Server Edition 96GB can run baichuan inc Baichuan M2 32B with a C grade (Runs well). Expected decode speed: 68.7 tok/s.

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

baichuan inc Baichuan M2 32B (32B parameters) requires approximately 34.1 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 RTX PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, baichuan inc Baichuan M2 32B achieves approximately 68.7 tokens per second decode speed with a time-to-first-token of 2817ms using Q4_K_M quantization.

Can RTX PRO 6000 Blackwell Server Edition 96GB run baichuan inc Baichuan M2 32B for coding?

For coding workloads, baichuan inc Baichuan M2 32B on RTX PRO 6000 Blackwell Server Edition 96GB receives a C grade with 68.7 tok/s and 280K context.

What context window can baichuan inc Baichuan M2 32B use on RTX PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, baichuan inc Baichuan M2 32B can safely use up to 280K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX PRO 6000 Blackwell Server Edition 96GB See all hardware for baichuan inc Baichuan M2 32B

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