Is unified memory on MacBook Pro M3 Max 128GB as fast as VRAM for GLM-5.2?

Not always. MacBook Pro M3 Max 128GB 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.

Can GLM-5.2 run on MacBook Pro M3 Max 128GB?

Q: What should I upgrade first if GLM-5.2 feels slow on MacBook Pro M3 Max 128GB?

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.

NO — Won't Fit

F0Won't run

Estimated from fit model

GLM-5.2 needs ~494.8 GB but MacBook Pro M3 Max 128GB only has 92.2 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) — 494.8 GB, exceeds 92.2 GB available

494.8 GB required92.2 GB available

537% VRAM needed

402.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

494.8 GB / 92.2 GB

Offload

80%

Memory breakdown

Weights459.5 GB

KV Cache19.0 GB

Runtime2.4 GB

Headroom13.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGLM-5.2 on MacBook Pro M3 Max 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 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 494.8 GB, but this setup only exposes 92.2 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	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Inference speed

GLM-5.2 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for GLM-5.2 at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~2 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?
RTX 5090 32GB	32 GB	Q4_K_M	2.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0

Quantization options

How GLM-5.2 (753.2999877929688B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	293.8 GB	Low	F0
Q3_K_S	3	369.1 GB	Low	F0
NVFP4	4

Frequently asked questions

Can MacBook Pro M3 Max 128GB run GLM-5.2?

No, GLM-5.2 requires more memory than MacBook Pro M3 Max 128GB provides.

How much VRAM does GLM-5.2 need?

GLM-5.2 (753.2999877929688B parameters) requires approximately 494.8 GB of memory with Q4_K_M quantization.

What is the best quantization for GLM-5.2?

The recommended quantization for GLM-5.2 is Q4_K_M, which balances quality and memory efficiency.

What speed will GLM-5.2 run at on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, GLM-5.2 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 128GB run GLM-5.2 for coding?

For coding workloads, GLM-5.2 on MacBook Pro M3 Max 128GB receives a F grade with 2.0 tok/s and 4K context.

What context window can GLM-5.2 use on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, GLM-5.2 can safely use up to 4K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.

What should I upgrade first if GLM-5.2 feels slow on MacBook Pro M3 Max 128GB?

See all results for MacBook Pro M3 Max 128GB See all hardware for GLM-5.2

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