Can Leanstral 119B A6B run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

S85Excellent

○Estimated from fit model

Leanstral 119B A6B needs ~111.4 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~17 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) — 111.4 GB, 17.0 tok/s, Runs well

111.4 GB required184.3 GB available

60% VRAM used

Fit status

Runs well

Decode

17.0 tok/s

TTFT

11412 ms

Safe context

149K

Memory

111.4 GB / 184.3 GB

Memory breakdown

Weights72.6 GB

KV Cache8.8 GB

Runtime2.4 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsLeanstral 119B A6B on Mac Studio M3 Ultra 256GB
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: 17.0 tok/s decode · 11.4s TTFT (warm) · 42 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	17.0 tok/s	6225 ms	149K
Coding	S	Runs well	17.0 tok/s	11412 ms	149K
Agentic Coding	S	Runs well	17.0 tok/s	16599 ms	149K
Reasoning	S	Runs well	17.0 tok/s	13487 ms	149K
RAG	S	Runs well	17.0 tok/s	20749 ms	149K

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A77
Q3_K_S	3	58.3 GB	Low	A79
NVFP4	4	66.6 GB	Medium	A80
Q4_K_M	4	72.6 GB	Medium	A80
Q5_K_M	5	85.7 GB	High	A82
Q6_K	6	97.6 GB	High	A83
Q8_0Best for your GPU	8	127.3 GB	Very High	A84
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 Mac Studio M3 Ultra 256GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	6.5 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	17 tok/s
👁 Mistral AI Pixtral Large 124B	124B	S	6.4 tok/s
MiniMax M2.7	230B	A	9.8 tok/s

Frequently asked questions

See all results for Mac Studio M3 Ultra 256GB See all hardware for Leanstral 119B A6B

URL: https://willitrunai.com/can-run/leanstral-119b-a6b-on-m3-ultra-256gb

⇱ Leanstral 119B A6B on Mac Studio M3 Ultra 256GB? YES