Will It Run AI

Can Qwen 2.5 Coder 32B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A76Great
Estimated from fit model

Qwen 2.5 Coder 32B needs ~37.1 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~112 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) — 37.1 GB, 111.5 tok/s, Runs well
37.1 GB required128.0 GB available
29% VRAM used

Fit status

Runs well

Decode

111.5 tok/s

TTFT

1736 ms

Safe context

131K

Memory

37.1 GB / 128.0 GB

Memory breakdown

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

See how fast it feels

See how fast it feelsQwen 2.5 Coder 32B on Intel Data Center GPU Max 1550 128GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 111.5 tok/s decode · 1.7s TTFT (warm) · 279 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
ChatARuns well111.5 tok/s947 ms131K
CodingARuns well111.5 tok/s1736 ms131K
Agentic CodingARuns well111.5 tok/s2525 ms131K
ReasoningARuns well111.5 tok/s2051 ms131K
RAGARuns well111.5 tok/s3156 ms131K

Quantization options

How Qwen 2.5 Coder 32B (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
LowB66
Q3_K_S
3
15.7 GB
LowB67
NVFP4
4
17.9 GB
MediumB67
Q4_K_M
4
19.5 GB
MediumB67
Q5_K_M
5
23.0 GB
HighB67
Q6_K
6
26.2 GB
HighB68
Q8_0
8
34.2 GB
Very HighB69
F16Best for your GPU
16
65.6 GB
MaximumA74

Get started

Copy-paste commands to run Qwen 2.5 Coder 32B on your machine.

Run

ollama run qwen2.5-coder

Your hardware

More models your Intel Data Center GPU Max 1550 128GB can run

ModelParamsGradeDecodeCapabilities
MistralDevstral 2 123B Instruct123BS29.2 tok/s
AlibabaQwen 3.5 122B A10B122BS81 tok/s
AlibabaQwen 3.6 35B A3B35BS256.2 tok/s
AlibabaQwen 3.5 35B A3B35BS278.6 tok/s
MistralMistral Small 4 119B119BS87.9 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Qwen 2.5 Coder 32B?

Yes, Intel Data Center GPU Max 1550 128GB can run Qwen 2.5 Coder 32B with a A grade (Runs well). Expected decode speed: 111.5 tok/s.

How much VRAM does Qwen 2.5 Coder 32B need?

Qwen 2.5 Coder 32B (32B parameters) requires approximately 37.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 32B?

The recommended quantization for Qwen 2.5 Coder 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 32B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Qwen 2.5 Coder 32B achieves approximately 111.5 tokens per second decode speed with a time-to-first-token of 1736ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Qwen 2.5 Coder 32B for coding?

For coding workloads, Qwen 2.5 Coder 32B on Intel Data Center GPU Max 1550 128GB receives a A grade with 111.5 tok/s and 131K context.

What context window can Qwen 2.5 Coder 32B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Qwen 2.5 Coder 32B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 2.5 Coder 32B 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 Qwen 2.5 Coder 32B?

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 Qwen 2.5 Coder 32B
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