Can Leanstral 119B A6B run on MacBook Pro M2 Max 96GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Leanstral 119B A6B needs ~94.1 GB but MacBook Pro M2 Max 96GB only has 69.1 GB. Try a smaller quantization or lighter model.

Runtime: vLLMCapacity: No fitBandwidth: LowStack: OptimizedBottleneck: Memory capacity

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) — 94.1 GB, exceeds 69.1 GB available

94.1 GB required69.1 GB available

136% VRAM needed

25.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.2 tok/s

TTFT

46165 ms

Safe context

Memory

94.1 GB / 69.1 GB

Offload

30%

Memory breakdown

Weights72.6 GB

KV Cache8.8 GB

Runtime2.4 GB

Headroom10.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLeanstral 119B A6B on MacBook Pro M2 Max 96GB
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: 4.2 tok/s decode · 46.2s TTFT (warm) · 11 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 94.1 GB, but this setup only exposes 69.1 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	4.5 tok/s	23562 ms	4K
Coding	F	Too heavy	4.2 tok/s	46165 ms	4K
Agentic Coding	F	Too heavy	3.7 tok/s	76019 ms	4K
Reasoning	F	Too heavy	4.2 tok/s	54559 ms	4K
RAG	F	Too heavy	3.7 tok/s	95024 ms	4K

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	46.4 GB	Low	A84
Q3_K_S	3	58.3 GB	Low	F0

Upgrade options

Hardware that runs Leanstral 119B A6B well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+160)819 GB/s (+419)

Makes the model fit on the accelerator instead of staying completely out of reach.17 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$6,999 MSRP

AMD Instinct MI350X 288GBBest value

288 GB VRAM (+192)8000 GB/s (+7600)

Makes the model fit on the accelerator instead of staying completely out of reach.177.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$8,000 MSRP

AMD Instinct MI300A 128GBBiggest leap

128 GB VRAM (+32)5300 GB/s (+4900)

Makes the model fit on the accelerator instead of staying completely out of reach.113 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$12,000 MSRP

Frequently asked questions

See all results for MacBook Pro M2 Max 96GB See all hardware for Leanstral 119B A6B

URL: https://willitrunai.com/can-run/leanstral-119b-a6b-on-m2-max-96gb

⇱

Can Leanstral 119B A6B run on MacBook Pro M2 Max 96GB?

NO — Won't Fit

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Hardware that runs Leanstral 119B A6B well

Frequently asked questions