Will It Run AI

Can Baichuan M2 32B Q4 K M run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

C48Usable
Estimated from fit model

Baichuan M2 32B Q4 K M needs ~37.0 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~103 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 37.0 GB, 103.3 tok/s, Runs well
37.0 GB required128.0 GB available
29% VRAM used

Fit status

Runs well

Decode

103.3 tok/s

TTFT

1875 ms

Safe context

404K

Memory

37.0 GB / 128.0 GB

Memory breakdown

Weights19.5 GB
KV Cache3.8 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsBaichuan M2 32B Q4 K M on Intel Data Center GPU Max 1550 128GB
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: 103.3 tok/s decode · 1.9s TTFT (warm) · 258 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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

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.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well103.3 tok/s1022 ms404K
CodingCRuns well103.3 tok/s1875 ms404K
Agentic CodingCRuns well103.3 tok/s2727 ms404K
ReasoningCRuns well103.3 tok/s2215 ms404K
RAGCRuns well103.3 tok/s3408 ms404K

Quantization options

How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowD38
Q3_K_S
3
15.7 GB
LowD38
NVFP4
4
17.9 GB
MediumD39
Q4_K_M
4
19.5 GB
MediumD39
Q5_K_M
5
23.0 GB
HighD39
Q6_K
6
26.2 GB
HighD39
Q8_0
8
34.2 GB
Very HighC41
F16Best for your GPU
16
65.6 GB
MaximumC46

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

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Baichuan M2 32B Q4 K M?

Yes, Intel Data Center GPU Max 1550 128GB can run Baichuan M2 32B Q4 K M with a C grade (Runs well). Expected decode speed: 103.3 tok/s.

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

Baichuan M2 32B Q4 K M (32B parameters) requires approximately 37.0 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 Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Baichuan M2 32B Q4 K M achieves approximately 103.3 tokens per second decode speed with a time-to-first-token of 1875ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Baichuan M2 32B Q4 K M for coding?

For coding workloads, Baichuan M2 32B Q4 K M on Intel Data Center GPU Max 1550 128GB receives a C grade with 103.3 tok/s and 404K context.

What context window can Baichuan M2 32B Q4 K M use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Baichuan M2 32B Q4 K M can safely use up to 404K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Baichuan M2 32B Q4 K M feels slow on Intel Data Center GPU Max 1550 128GB?

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.

Would CUDA be a better path than Intel Data Center GPU Max 1550 128GB 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.

See all results for Intel Data Center GPU Max 1550 128GBSee all hardware for Baichuan M2 32B Q4 K M
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