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URL: https://willitrunai.com/can-run/hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf-on-a6000-48gb

⇱ baichuan inc Baichuan M2 32B on RTX A6000 48GB? YES

Can baichuan inc Baichuan M2 32B run on RTX A6000 48GB?

YES — Runs Great

C51Usable
○Estimated from fit model

baichuan inc Baichuan M2 32B needs ~29.3 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Share:

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, 29.9 tok/s, Runs well
29.3 GB required48.0 GB available
61% VRAM used

Fit status

Runs well

Decode

29.9 tok/s

TTFT

6475 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 A6000 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: 29.9 tok/s decode · 6.5s TTFT (warm) · 75 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 well29.9 tok/s3532 ms96K
CodingCRuns well29.9 tok/s6475 ms96K
Agentic CodingCRuns well29.9 tok/s9418 ms96K
ReasoningCRuns well29.9 tok/s7652 ms96K
RAGCRuns well29.9 tok/s11772 ms96K

Quantization options

How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on RTX A6000 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

See all results for RTX A6000 48GBSee all hardware for baichuan inc Baichuan M2 32B