Will It Run AI

Can baichuan inc Baichuan M2 32B run on RTX PRO 5000 Blackwell 48GB?

YES — Runs Great

C53Usable
Estimated from fit model

baichuan inc Baichuan M2 32B needs ~29.3 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~58 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 29.3 GB, 57.8 tok/s, Runs well
29.3 GB required48.0 GB available
61% VRAM used

Fit status

Runs well

Decode

57.8 tok/s

TTFT

3347 ms

Safe context

96K

Memory

29.3 GB / 48.0 GB

Memory breakdown

Weights19.5 GB
KV Cache3.8 GB
Runtime1.2 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsbaichuan inc Baichuan M2 32B on RTX PRO 5000 Blackwell 48GB
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: 57.8 tok/s decode · 3.3s TTFT (warm) · 145 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well57.8 tok/s1826 ms96K
CodingCRuns well57.8 tok/s3347 ms96K
Agentic CodingCRuns well57.8 tok/s4869 ms96K
ReasoningCRuns well57.8 tok/s3956 ms96K
RAGCRuns well57.8 tok/s6086 ms96K

Quantization options

How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowC43
Q3_K_S
3
15.7 GB
LowC44
NVFP4
4
17.9 GB
MediumC45
Q4_K_M
4
19.5 GB
MediumC46
Q5_K_M
5
23.0 GB
HighC47
Q6_K
6
26.2 GB
HighC48
Q8_0Best for your GPU
8
34.2 GB
Very HighC47
F16
16
65.6 GB
MaximumF0

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 5000 Blackwell 48GB run baichuan inc Baichuan M2 32B?

Yes, RTX PRO 5000 Blackwell 48GB can run baichuan inc Baichuan M2 32B with a C grade (Runs well). Expected decode speed: 57.8 tok/s.

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

baichuan inc Baichuan M2 32B (32B parameters) requires approximately 29.3 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 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, baichuan inc Baichuan M2 32B achieves approximately 57.8 tokens per second decode speed with a time-to-first-token of 3347ms using Q4_K_M quantization.

Can RTX PRO 5000 Blackwell 48GB run baichuan inc Baichuan M2 32B for coding?

For coding workloads, baichuan inc Baichuan M2 32B on RTX PRO 5000 Blackwell 48GB receives a C grade with 57.8 tok/s and 96K context.

What context window can baichuan inc Baichuan M2 32B use on RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, baichuan inc Baichuan M2 32B can safely use up to 96K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX PRO 5000 Blackwell 48GBSee all hardware for baichuan inc Baichuan M2 32B
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