Will It Run AI

Can Leanstral 119B A6B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

S92Excellent
Estimated from fit model

Leanstral 119B A6B needs ~96.6 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~61 tok/s.

Runtime: vLLMCapacity: RoomyBandwidth: HighStack: OptimizedBottleneck: 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) — 96.6 GB, 61.4 tok/s, Runs well
96.6 GB required128.0 GB available
75% VRAM used

Fit status

Runs well

Decode

61.4 tok/s

TTFT

3152 ms

Safe context

73K

Memory

96.6 GB / 128.0 GB

Memory breakdown

Weights72.6 GB
KV Cache8.8 GB
Runtime2.4 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsLeanstral 119B A6B 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: 61.4 tok/s decode · 3.2s TTFT (warm) · 154 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 well61.4 tok/s1720 ms73K
CodingSRuns well61.4 tok/s3152 ms73K
Agentic CodingSTight fit61.4 tok/s4585 ms73K
ReasoningSRuns well61.4 tok/s3726 ms73K
RAGSTight fit61.4 tok/s5732 ms73K

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
46.4 GB
LowA80
Q3_K_S
3
58.3 GB
LowA82
NVFP4
4
66.6 GB
MediumA83
Q4_K_M
4
72.6 GB
MediumA84
Q5_K_M
5
85.7 GB
HighA84
Q6_KBest for your GPU
6
97.6 GB
HighA84
Q8_0
8
127.3 GB
Very HighF0
F16
16
244.0 GB
MaximumF0

Get started

Copy-paste commands to run Leanstral 119B A6B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "mistralai/Leanstral-2603" \ --hf-file "Leanstral-2603-Q4_K_M.gguf" \ -c 4096 -ngl 99

Your hardware

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

ModelParamsGradeDecodeCapabilities
MistralDevstral 2 123B Instruct123BS23.4 tok/s
AlibabaQwen 3.5 122B A10B122BS61.6 tok/s
Mistral AIPixtral Large 124B124BS23.2 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Leanstral 119B A6B?

Yes, Intel Data Center GPU Max 1550 128GB can run Leanstral 119B A6B with a S grade (Runs well). Expected decode speed: 61.4 tok/s.

How much VRAM does Leanstral 119B A6B need?

Leanstral 119B A6B (119B parameters) requires approximately 96.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Leanstral 119B A6B?

The recommended quantization for Leanstral 119B A6B is Q4_K_M, which balances quality and memory efficiency.

What speed will Leanstral 119B A6B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Leanstral 119B A6B achieves approximately 61.4 tokens per second decode speed with a time-to-first-token of 3152ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Leanstral 119B A6B for coding?

For coding workloads, Leanstral 119B A6B on Intel Data Center GPU Max 1550 128GB receives a S grade with 61.4 tok/s and 73K context.

What context window can Leanstral 119B A6B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Leanstral 119B A6B can safely use up to 73K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Leanstral 119B A6B 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 Leanstral 119B A6B?

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 Leanstral 119B A6B
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