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

Inference speed

Leanstral 119B A6B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Leanstral 119B A6B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~17 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	17.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	11.9	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	11.3	Too big
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	8.8	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	7.4	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	6.7	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	5.8	Too big
RTX 5090 32GB	32 GB	Q4_K_M	5.5	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	5.1	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	3.7	Too big
RTX 4090 24GB	24 GB	Q4_K_M	3.5	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	3.2	Too big
RTX 3090 24GB	24 GB	Q4_K_M	3.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.8	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.6	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.4	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

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
Devstral 2 123B Instruct	123B	S	6.5 tok/s
Qwen 3.5 122B A10B	122B	S	17 tok/s
Pixtral Large 124B	124B	S	6.4 tok/s
MiniMax M2.7	230B	A	9.8 tok/s

Frequently asked questions

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

Yes, Mac Studio M3 Ultra 256GB can run Leanstral 119B A6B with a S grade (Runs well). Expected decode speed: 17.0 tok/s.

How much VRAM does Leanstral 119B A6B need?

Leanstral 119B A6B (119B parameters) requires approximately 111.4 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 Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Leanstral 119B A6B achieves approximately 17.0 tokens per second decode speed with a time-to-first-token of 11412ms using Q4_K_M quantization.

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

For coding workloads, Leanstral 119B A6B on Mac Studio M3 Ultra 256GB receives a S grade with 17.0 tok/s and 149K context.

What context window can Leanstral 119B A6B use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Leanstral 119B A6B can safely use up to 149K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for Leanstral 119B A6B?

Not always. Mac Studio M3 Ultra 256GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

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

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