Would CUDA be a better path than Intel Arc Pro B50 16GB for Baichuan M2 32B Q4 K M?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

Can Baichuan M2 32B Q4 K M run on Intel Arc Pro B50 16GB?

Q: What should I upgrade first if Baichuan M2 32B Q4 K M feels slow on Intel Arc Pro B50 16GB?

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.

YES — With Q2_K

D34Poor

Estimated from fit model

Baichuan M2 32B Q4 K M needs ~18.7 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q2_K quantization, expect ~5 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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.

Baichuan M2 32B Q4 K M at Q4_K_M needs 25.8 GB — too much for Intel Arc Pro B50 16GB (16.0 GB). Runs at Q2_K (18.7 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 25.8 GB, exceeds 16.0 GB available

25.8 GB required16.0 GB available

161% VRAM needed

9.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

25.8 GB / 16.0 GB

Offload

40%

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom1.6 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 Intel Arc Pro B50 16GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 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.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

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.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.1 tok/s	50507 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 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

Get started

Copy-paste commands to run Baichuan M2 32B Q4 K M on your machine.

Run

lms load hf-baichuan-inc--baichuan-m2-32b-q4-k-m-gguf && lms server start

Upgrade options

Hardware that runs Baichuan M2 32B Q4 K M well

Intel Arc Pro B60 24GBBest value

24 GB VRAM (+8)456 GB/s (+232)

Makes the model fit on the accelerator instead of staying completely out of reach.7.8 tok/s decode

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

Raises estimated decode speed by about 290%.

~$599 MSRP

NVIDIA A100 40GBBiggest leap

40 GB VRAM (+24)1555 GB/s (+1331)

Makes the model fit on the accelerator instead of staying completely out of reach.66.9 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.

~$10,000 MSRP

Intel Data Center GPU Max 1550 128GBBudget pick

128 GB VRAM (+112)3200 GB/s (+2976)

Makes the model fit on the accelerator instead of staying completely out of reach.103.3 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.

~$15,000 MSRP

Gaudi 3 128GBIntel upgrade

128 GB VRAM (+112)3700 GB/s (+3476)

Makes the model fit on the accelerator instead of staying completely out of reach.132.7 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.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc Pro B50 16GB run Baichuan M2 32B Q4 K M?

Yes, Intel Arc Pro B50 16GB can run Baichuan M2 32B Q4 K M at Q2_K quantization (Very compromised (needs ~1.8 GB host RAM)). The recommended Q4_K_M requires 25.8 GB which exceeds available memory, but at Q2_K it needs only 18.7 GB. Expected decode speed: 4.6 tok/s.

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

Baichuan M2 32B Q4 K M (32B parameters) requires approximately 25.8 GB at Q4_K_M quantization. On Intel Arc Pro B50 16GB, it fits at Q2_K using 18.7 GB.

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

The recommended quantization is Q4_K_M, but on Intel Arc Pro B50 16GB the best fitting quantization is Q2_K, which uses 18.7 GB.

What speed will Baichuan M2 32B Q4 K M run at on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Baichuan M2 32B Q4 K M achieves approximately 4.6 tokens per second decode speed with a time-to-first-token of 42378ms using Q2_K quantization.

Can Intel Arc Pro B50 16GB run Baichuan M2 32B Q4 K M for coding?

For coding workloads, Baichuan M2 32B Q4 K M on Intel Arc Pro B50 16GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Baichuan M2 32B Q4 K M use on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Baichuan M2 32B Q4 K M can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc Pro B50 16GB See all hardware for Baichuan M2 32B Q4 K M

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