Can Baichuan M2 32B Q4 K M run on RTX 4000 Ada Laptop 12GB?

Q: What should I upgrade first if Baichuan M2 32B Q4 K M feels slow on RTX 4000 Ada Laptop 12GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

Baichuan M2 32B Q4 K M needs ~25.7 GB but RTX 4000 Ada Laptop 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 25.7 GB, exceeds 12.0 GB available

25.7 GB required12.0 GB available

214% VRAM needed

13.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.4 tok/s

TTFT

79192 ms

Safe context

Memory

25.7 GB / 12.0 GB

Offload

50%

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsBaichuan M2 32B Q4 K M on RTX 4000 Ada Laptop 12GB
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: 2.4 tok/s decode · 79.2s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 25.7 GB, but this setup only exposes 12.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.9 tok/s	36822 ms	4K
Coding	F	Too heavy	2.4 tok/s	79192 ms	4K
Agentic Coding	F	Too heavy	2.4 tok/s	116202 ms	4K
Reasoning	F	Too heavy	2.4 tok/s	93591 ms	4K
RAG	F	Too heavy	2.4 tok/s	145253 ms	4K

Quantization options

How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	F0
Q3_K_S	3	15.7 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Baichuan M2 32B Q4 K M well

RTX 3090 24GBBest value

24 GB VRAM (+12)936 GB/s (+504)

Makes the model fit on the accelerator instead of staying completely out of reach.19.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 725%.

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+12)1008 GB/s (+576)

Makes the model fit on the accelerator instead of staying completely out of reach.23.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 867%.

~$1,599 MSRP

RTX 5090 32GBBudget pick

32 GB VRAM (+20)1792 GB/s (+1360)

Makes the model fit on the accelerator instead of staying completely out of reach.61.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,999 MSRP

Frequently asked questions

Can RTX 4000 Ada Laptop 12GB run Baichuan M2 32B Q4 K M?

No, Baichuan M2 32B Q4 K M requires more memory than RTX 4000 Ada Laptop 12GB provides.

How much VRAM does Baichuan M2 32B Q4 K M need?

Baichuan M2 32B Q4 K M (32B parameters) requires approximately 25.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Baichuan M2 32B Q4 K M?

The recommended quantization for Baichuan M2 32B Q4 K M is Q4_K_M, which balances quality and memory efficiency.

What speed will Baichuan M2 32B Q4 K M run at on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Baichuan M2 32B Q4 K M achieves approximately 2.4 tokens per second decode speed with a time-to-first-token of 79192ms using Q4_K_M quantization.

Can RTX 4000 Ada Laptop 12GB run Baichuan M2 32B Q4 K M for coding?

For coding workloads, Baichuan M2 32B Q4 K M on RTX 4000 Ada Laptop 12GB receives a F grade with 2.4 tok/s and 4K context.

What context window can Baichuan M2 32B Q4 K M use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Baichuan M2 32B Q4 K M can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada Laptop 12GB See all hardware for Baichuan M2 32B Q4 K M

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