Will It Run AI

Can Nemotron 3 Nano 30B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

S88Excellent
Estimated from fit model

Nemotron 3 Nano 30B needs ~34.4 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~118 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) — 34.4 GB, 118.4 tok/s, Runs well
34.4 GB required128.0 GB available
27% VRAM used

Fit status

Runs well

Decode

118.4 tok/s

TTFT

1635 ms

Safe context

131K

Memory

34.4 GB / 128.0 GB

Memory breakdown

Weights18.3 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsNemotron 3 Nano 30B 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: 118.4 tok/s decode · 1.6s TTFT (warm) · 296 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 well118.4 tok/s892 ms131K
CodingSRuns well118.4 tok/s1635 ms131K
Agentic CodingSRuns well118.4 tok/s2378 ms131K
ReasoningSRuns well110.2 tok/s2077 ms131K
RAGSRuns well118.4 tok/s2972 ms131K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
11.7 GB
LowA79
Q3_K_S
3
14.7 GB
LowA79
NVFP4
4
16.8 GB
MediumA79
Q4_K_M
4
18.3 GB
MediumA79
Q5_K_M
5
21.6 GB
HighA79
Q6_K
6
24.6 GB
HighA80
Q8_0
8
32.1 GB
Very HighA81
F16Best for your GPU
16
61.5 GB
MaximumS86

Get started

Copy-paste commands to run Nemotron 3 Nano 30B on your machine.

Run

ollama run nemotron-nano:30b

Your hardware

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

ModelParamsGradeDecodeCapabilities
MistralDevstral 2 123B Instruct123BS29.2 tok/s
AlibabaQwen3-Coder 30B A3B Instruct30.5BS304.8 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

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Nemotron 3 Nano 30B?

Yes, Intel Data Center GPU Max 1550 128GB can run Nemotron 3 Nano 30B with a S grade (Runs well). Expected decode speed: 118.4 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 34.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron 3 Nano 30B?

The recommended quantization for Nemotron 3 Nano 30B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron 3 Nano 30B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Nemotron 3 Nano 30B achieves approximately 118.4 tokens per second decode speed with a time-to-first-token of 1635ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on Intel Data Center GPU Max 1550 128GB receives a S grade with 118.4 tok/s and 131K context.

What context window can Nemotron 3 Nano 30B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Nemotron 3 Nano 30B 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 Nemotron 3 Nano 30B 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 Nemotron 3 Nano 30B?

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 Nemotron 3 Nano 30B
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