Will It Run AI

Can Qwen3-Coder-Next run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

S92Excellent
Estimated from fit model

Qwen3-Coder-Next needs ~64.0 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~136 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) — 64.0 GB, 136.1 tok/s, Runs well
64.0 GB required128.0 GB available
50% VRAM used

Fit status

Runs well

Decode

136.1 tok/s

TTFT

1422 ms

Safe context

256K

Memory

64.0 GB / 128.0 GB

Memory breakdown

Weights48.8 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next 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: 136.1 tok/s decode · 1.4s TTFT (warm) · 340 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 well136.1 tok/s776 ms256K
CodingSRuns well136.1 tok/s1422 ms256K
Agentic CodingSRuns well136.1 tok/s2068 ms256K
ReasoningSRuns well136.1 tok/s1681 ms256K
RAGSRuns well136.1 tok/s2586 ms256K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
31.2 GB
LowA81
Q3_K_S
3
39.2 GB
LowA82
NVFP4
4
44.8 GB
MediumA83
Q4_K_M
4
48.8 GB
MediumA84
Q5_K_M
5
57.6 GB
HighS85
Q6_K
6
65.6 GB
HighS87
Q8_0Best for your GPU
8
85.6 GB
Very HighS88
F16
16
164.0 GB
MaximumF0

Get started

Copy-paste commands to run Qwen3-Coder-Next on your machine.

Run

ollama run qwen3-coder-next

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
MistralMistral Small 4 119B119BS87.9 tok/s
OpenAIGPT-OSS 120B117BS30.7 tok/s
CohereCommand A 111B111BS32.5 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Qwen3-Coder-Next?

Yes, Intel Data Center GPU Max 1550 128GB can run Qwen3-Coder-Next with a S grade (Runs well). Expected decode speed: 136.1 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 64.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder-Next?

The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder-Next run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Qwen3-Coder-Next achieves approximately 136.1 tokens per second decode speed with a time-to-first-token of 1422ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on Intel Data Center GPU Max 1550 128GB receives a S grade with 136.1 tok/s and 256K context.

What context window can Qwen3-Coder-Next use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Qwen3-Coder-Next can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3-Coder-Next 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 Qwen3-Coder-Next?

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 Qwen3-Coder-Next
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