Can Nemotron Cascade 2 30B A3B run on Mac mini M4 64GB?

YES — Runs Great

S87Excellent

Estimated from fit model

Nemotron Cascade 2 30B A3B needs ~29.0 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 29.0 GB, 13.4 tok/s, Runs well

29.0 GB required46.1 GB available

63% VRAM used

Fit status

Runs well

Decode

13.4 tok/s

TTFT

14495 ms

Safe context

109K

Memory

29.0 GB / 46.1 GB

Memory breakdown

Weights18.3 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsNemotron Cascade 2 30B A3B on Mac mini M4 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: 13.4 tok/s decode · 14.5s TTFT (warm) · 33 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	S	Runs well	13.4 tok/s	7906 ms	109K
Coding	S	Runs well	13.4 tok/s	14495 ms	109K
Agentic Coding	S	Runs well	13.4 tok/s	21084 ms	109K
Reasoning	S	Runs well	13.4 tok/s	17130 ms	109K
RAG	S	Runs well	13.4 tok/s	26355 ms	109K

Inference speed

Nemotron Cascade 2 30B A3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Nemotron Cascade 2 30B A3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~186 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	185.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	86.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	84.8	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	76.5	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	72.6	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	71.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	68.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	37.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	34.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	32.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	30.1	Too big
RTX 4070 12GB	12 GB	Q4_K_M	10.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	6.6	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.6	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 Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A82
Q3_K_S	3	14.7 GB	Low	A83
NVFP4	4	16.8 GB	Medium	A83
Q4_K_M	4	18.3 GB	Medium	A84
Q5_K_M	5	21.6 GB	High	A85
Q6_K	6	24.6 GB	High	S86
Q8_0Best for your GPU	8	32.1 GB	Very High	S86
F16	16	61.5 GB	Maximum	F0

Get started

Copy-paste commands to run Nemotron Cascade 2 30B A3B on your machine.

Run

ollama run nemotron-cascade-2

Your hardware

More models your Mac mini M4 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	13.1 tok/s
Qwen 3.6 35B A3B	35B	S	12.1 tok/s
Qwen 3.5 35B A3B	35B	S	13.1 tok/s
Qwen 3 32B	32B	S	8.7 tok/s
Qwen 3 30B A3B	30.5B	S	13.1 tok/s

Frequently asked questions

Can Mac mini M4 64GB run Nemotron Cascade 2 30B A3B?

Yes, Mac mini M4 64GB can run Nemotron Cascade 2 30B A3B with a S grade (Runs well). Expected decode speed: 13.4 tok/s.

How much VRAM does Nemotron Cascade 2 30B A3B need?

Nemotron Cascade 2 30B A3B (30B parameters) requires approximately 29.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Cascade 2 30B A3B?

The recommended quantization for Nemotron Cascade 2 30B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Cascade 2 30B A3B run at on Mac mini M4 64GB?

On Mac mini M4 64GB, Nemotron Cascade 2 30B A3B achieves approximately 13.4 tokens per second decode speed with a time-to-first-token of 14495ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Nemotron Cascade 2 30B A3B for coding?

For coding workloads, Nemotron Cascade 2 30B A3B on Mac mini M4 64GB receives a S grade with 13.4 tok/s and 109K context.

What context window can Nemotron Cascade 2 30B A3B use on Mac mini M4 64GB?

On Mac mini M4 64GB, Nemotron Cascade 2 30B A3B can safely use up to 109K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for Nemotron Cascade 2 30B A3B?

Not always. Mac mini M4 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 Mac mini M4 64GB See all hardware for Nemotron Cascade 2 30B A3B

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