Can Mistral Small 4 119B run on MacBook Pro M4 Max 64GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Mistral Small 4 119B needs ~85.8 GB but MacBook Pro M4 Max 64GB only has 46.1 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: MediumStack: StandardBottleneck: 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) — 85.8 GB, exceeds 46.1 GB available

85.8 GB required46.1 GB available

186% VRAM needed

39.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.7 tok/s

TTFT

18104 ms

Safe context

Memory

85.8 GB / 46.1 GB

Offload

50%

Memory breakdown

Weights72.6 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMistral Small 4 119B on MacBook Pro M4 Max 64GB
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: 10.7 tok/s decode · 18.1s TTFT (warm) · 27 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 85.8 GB, but this setup only exposes 46.1 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	11.1 tok/s	9538 ms	4K
Coding	F	Too heavy	10.7 tok/s	18104 ms	4K
Agentic Coding	F	Too heavy	10.5 tok/s	26926 ms	4K
Reasoning	F	Too heavy	10.7 tok/s	21396 ms	4K
RAG	F	Too heavy	10.5 tok/s	33658 ms	4K

Inference speed

Mistral Small 4 119B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Mistral Small 4 119B 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 ~38 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	37.6	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	30.8	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	29.3	Offloads
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	22.9	Offloads
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	11.9	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	10.7	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	8.0	Too big
RTX 5090 32GB	32 GB	Q4_K_M	7.9	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	7.5	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	6.8	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	6.8	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	6.4	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	6.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	5.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	4.5	Too big
RTX 3090 24GB	24 GB	Q4_K_M	4.3	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	4.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.5	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

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 Mistral Small 4 119B (119B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	F0
Q3_K_S	3	58.3 GB	Low	F0
NVFP4	4	66.6 GB	Medium	F0
Q4_K_M	4	72.6 GB	Medium	F0
Q5_K_M	5	85.7 GB	High	F0
Q6_K	6	97.6 GB	High	F0
Q8_0	8	127.3 GB	Very High	F0
F16	16	244.0 GB	Maximum	F0

升级选项

能流畅运行 Mistral Small 4 119B 的硬件

MacBook Pro M3 Max 128GB经济之选

128 GB Unified (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.16 tok/s 解码

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.

~$2,499 MSRP

Mac Studio M2 Ultra 128GB最佳性价比

128 GB Unified (+64)800 GB/s (+254)

Makes the model fit on the accelerator instead of staying completely out of reach.30.8 tok/s 解码

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.

~$3,999 MSRP

Mac Studio M1 Ultra 128GBApple 升级

128 GB Unified (+64)800 GB/s (+254)

Makes the model fit on the accelerator instead of staying completely out of reach.29.3 tok/s 解码

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.

~$3,999 MSRP

AMD Instinct MI250X 128GB最大飞跃

128 GB VRAM (+64)3200 GB/s (+2654)

Makes the model fit on the accelerator instead of staying completely out of reach.108.8 tok/s 解码

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.

~$15,000 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 64GB run Mistral Small 4 119B?

No, Mistral Small 4 119B requires more memory than MacBook Pro M4 Max 64GB provides.

How much VRAM does Mistral Small 4 119B need?

Mistral Small 4 119B (119B parameters) requires approximately 85.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 4 119B?

The recommended quantization for Mistral Small 4 119B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 4 119B run at on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Mistral Small 4 119B achieves approximately 10.7 tokens per second decode speed with a time-to-first-token of 18104ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 64GB run Mistral Small 4 119B for coding?

For coding workloads, Mistral Small 4 119B on MacBook Pro M4 Max 64GB receives a F grade with 10.7 tok/s and 4K context.

What context window can Mistral Small 4 119B use on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Mistral Small 4 119B can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Small 4 119B feels slow on MacBook Pro M4 Max 64GB?

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.

Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for Mistral Small 4 119B?

Not always. MacBook Pro M4 Max 64GB 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 MacBook Pro M4 Max 64GB See all hardware for Mistral Small 4 119B

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