Will It Run AI

Can baichuan inc Baichuan M2 32B run on Quadro RTX 6000 24GB?

BARELY — Tight on Memory

D37Poor
Estimated from fit model

baichuan inc Baichuan M2 32B needs ~26.6 GB VRAM. Quadro RTX 6000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
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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) — 26.6 GB, 13.9 tok/s, Very compromised (needs ~1.9 GB host RAM)
26.6 GB required24.0 GB available
111% VRAM needed

2.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.9 GB host RAM)

Decode

13.9 tok/s

TTFT

13971 ms

Safe context

5K

Memory

26.6 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights19.5 GB
KV Cache3.8 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsbaichuan inc Baichuan M2 32B on Quadro RTX 6000 24GB
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: 13.9 tok/s decode · 14.0s TTFT (warm) · 35 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload (needs ~0.5 GB host RAM)16.3 tok/s6489 ms5K
CodingDVery compromised (needs ~1.9 GB host RAM)13.9 tok/s13971 ms5K
Agentic CodingFToo heavy10.4 tok/s27155 ms5K
ReasoningDVery compromised (needs ~1.9 GB host RAM)13.9 tok/s16511 ms5K
RAGFToo heavy10.4 tok/s33944 ms5K

Quantization options

How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on Quadro RTX 6000 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowC50
Q3_K_S
3
15.7 GB
LowC49
NVFP4Best for your GPU
4
17.9 GB
MediumC49
Q4_K_M
4
19.5 GB
MediumF0
Q5_K_M
5
23.0 GB
HighF0
Q6_K
6
26.2 GB
HighF0
Q8_0
8
34.2 GB
Very HighF0
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

アップグレードオプション

baichuan inc Baichuan M2 32Bを快適に動かすハードウェア

NVIDIARTX 5090 32GBコスパ重視
32 GB VRAM (+8)1792 GB/s (+1120)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.39.9 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 187%.

〜$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBコスパ最良
32 GB VRAM (+8)896 GB/s (+224)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.38.6 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 178%.

〜$2,499 MSRP

NVIDIARTX 5000 Ada 32GBNVIDIAアップグレード
32 GB VRAM (+8)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.23.6 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 70%.

〜$4,000 MSRP

Frequently asked questions

Can Quadro RTX 6000 24GB run baichuan inc Baichuan M2 32B?

Yes, Quadro RTX 6000 24GB can run baichuan inc Baichuan M2 32B with a D grade (Very compromised (needs ~1.9 GB host RAM)). Expected decode speed: 13.9 tok/s.

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

baichuan inc Baichuan M2 32B (32B parameters) requires approximately 26.6 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 Quadro RTX 6000 24GB?

On Quadro RTX 6000 24GB, baichuan inc Baichuan M2 32B achieves approximately 13.9 tokens per second decode speed with a time-to-first-token of 13971ms using Q4_K_M quantization.

Can Quadro RTX 6000 24GB run baichuan inc Baichuan M2 32B for coding?

For coding workloads, baichuan inc Baichuan M2 32B on Quadro RTX 6000 24GB receives a D grade with 13.9 tok/s and 5K context.

What context window can baichuan inc Baichuan M2 32B use on Quadro RTX 6000 24GB?

On Quadro RTX 6000 24GB, baichuan inc Baichuan M2 32B can safely use up to 5K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if baichuan inc Baichuan M2 32B feels slow on Quadro RTX 6000 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for Quadro RTX 6000 24GBSee all hardware for baichuan inc Baichuan M2 32B
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