Will It Run AI

Can Qwen 3.5 122B A10B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

S99Excellent
Estimated from fit model

Qwen 3.5 122B A10B needs ~90.6 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~81 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) — 90.6 GB, 81.0 tok/s, Runs well
90.6 GB required128.0 GB available
71% VRAM used

Fit status

Runs well

Decode

81.0 tok/s

TTFT

2389 ms

Safe context

131K

Memory

90.6 GB / 128.0 GB

Memory breakdown

Weights74.4 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsQwen 3.5 122B A10B 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: 81.0 tok/s decode · 2.4s TTFT (warm) · 203 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
ChatSRuns well81.0 tok/s1303 ms131K
CodingSRuns well81.0 tok/s2389 ms131K
Agentic CodingSRuns well74.1 tok/s3800 ms131K
ReasoningSRuns well81.0 tok/s2823 ms131K
RAGSRuns well81.0 tok/s4343 ms131K

Quantization options

How Qwen 3.5 122B A10B (122B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
47.6 GB
LowS87
Q3_K_S
3
59.8 GB
LowS89
NVFP4
4
68.3 GB
MediumS90
Q4_K_M
4
74.4 GB
MediumS90
Q5_K_M
5
87.8 GB
HighS90
Q6_KBest for your GPU
6
100.0 GB
HighS90
Q8_0
8
130.5 GB
Very HighF0
F16
16
250.1 GB
MaximumF0

Get started

Copy-paste commands to run Qwen 3.5 122B A10B on your machine.

Run

lms load Qwen3.5-122B-A10B-Instruct && lms server start

Your hardware

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

ModelParamsGradeDecodeCapabilities
MistralDevstral 2 123B Instruct123BS29.2 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Qwen 3.5 122B A10B?

Yes, Intel Data Center GPU Max 1550 128GB can run Qwen 3.5 122B A10B with a S grade (Runs well). Expected decode speed: 81.0 tok/s.

How much VRAM does Qwen 3.5 122B A10B need?

Qwen 3.5 122B A10B (122B parameters) requires approximately 90.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 122B A10B?

The recommended quantization for Qwen 3.5 122B A10B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 122B A10B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Qwen 3.5 122B A10B achieves approximately 81.0 tokens per second decode speed with a time-to-first-token of 2389ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Qwen 3.5 122B A10B for coding?

For coding workloads, Qwen 3.5 122B A10B on Intel Data Center GPU Max 1550 128GB receives a S grade with 81.0 tok/s and 131K context.

What context window can Qwen 3.5 122B A10B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Qwen 3.5 122B A10B 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 3.5 122B A10B 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 3.5 122B A10B?

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 3.5 122B A10B
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