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

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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	61.4 tok/s	1720 ms	73K
Coding	S	Runs well	61.4 tok/s	3152 ms	73K
Agentic Coding	S	Tight fit	61.4 tok/s	4585 ms	73K
Reasoning	S	Runs well	61.4 tok/s	3726 ms	73K
RAG	S	Tight fit	61.4 tok/s	5732 ms	73K

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).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A80
Q3_K_S	3	58.3 GB	Low	A82
NVFP4	4	66.6 GB	Medium	A83
Q4_K_M	4	72.6 GB	Medium	A84
Q5_K_M	5	85.7 GB	High	A84
Q6_KBest for your GPU	6	97.6 GB	High	A84
Q8_0	8	127.3 GB	Very High	F0
F16	16	244.0 GB	Maximum	F0

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

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	23.4 tok/s
Qwen 3.5 122B A10B	122B	S	61.6 tok/s
Pixtral Large 124B	124B	S	23.2 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 128GB See all hardware for Leanstral 119B A6B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

61.4 tok/s

1720 ms

73K

Coding

Runs well

61.4 tok/s

3152 ms

73K

Agentic Coding

Tight fit

61.4 tok/s

4585 ms

73K

Reasoning

Runs well

61.4 tok/s

3726 ms

73K

RAG

Tight fit

61.4 tok/s

5732 ms

73K

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).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A80
Q3_K_S	3	58.3 GB	Low	A82
NVFP4	4	66.6 GB	Medium	A83
Q4_K_M	4	72.6 GB	Medium	A84
Q5_K_M	5	85.7 GB	High	A84
Q6_KBest for your GPU	6	97.6 GB	High	A84
Q8_0	8	127.3 GB	Very High	F0
F16	16	244.0 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

23.4 tok/s

Qwen 3.5 122B A10B

122B

61.6 tok/s

Pixtral Large 124B

124B

23.2 tok/s