Can Granite Code 20B run on MacBook Pro M4 32GB?

YES — Tight Fit

A75Great

Estimated — low-sample bucket· few comparable runs

Granite Code 20B needs ~19.7 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 19.7 GB, 9.9 tok/s, Tight fit

19.7 GB required23.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

9.9 tok/s

TTFT

19471 ms

Safe context

Memory

19.7 GB / 23.0 GB

Memory breakdown

Weights12.2 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsGranite Code 20B on MacBook Pro M4 32GB
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: 9.9 tok/s decode · 19.5s TTFT (warm) · 25 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	7.1 tok/s	14911 ms	8K
Coding	A	Tight fit	7.1 tok/s	27337 ms	8K
Agentic Coding	A	Runs with offload	7.1 tok/s	39763 ms	8K
Reasoning	A	Tight fit	7.1 tok/s	32307 ms	8K
RAG	A	Runs with offload	7.1 tok/s	49704 ms	8K

Quantization options

How Granite Code 20B (20B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	A77
Q3_K_S	3	9.8 GB	Low	A79
NVFP4	4	11.2 GB	Medium	A80
Q4_K_M	4	12.2 GB	Medium	A80
Q5_K_M	5	14.4 GB	High	A80
Q6_KBest for your GPU	6	16.4 GB	High	A79
Q8_0	8	21.4 GB	Very High	F0
F16	16	41.0 GB	Maximum	F0

Get started

Copy-paste commands to run Granite Code 20B on your machine.

Run

ollama run granite-code:20b

Your hardware

More models your MacBook Pro M4 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	11.7 tok/s
Qwen 3.5 27B	27B	S	8.6 tok/s
Qwen 3.6 27B	27B	S	7.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	12.4 tok/s
Qwen 3.5 35B A3B	35B	A	10.2 tok/s

Frequently asked questions

Can MacBook Pro M4 32GB run Granite Code 20B?

Yes, MacBook Pro M4 32GB can run Granite Code 20B with a A grade (Tight fit). Expected decode speed: 7.1 tok/s.

How much VRAM does Granite Code 20B need?

Granite Code 20B (20B parameters) requires approximately 19.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 20B?

The recommended quantization for Granite Code 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 20B run at on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Granite Code 20B achieves approximately 7.1 tokens per second decode speed with a time-to-first-token of 27337ms using Q4_K_M quantization.

Can MacBook Pro M4 32GB run Granite Code 20B for coding?

For coding workloads, Granite Code 20B on MacBook Pro M4 32GB receives a A grade with 7.1 tok/s and 8K context.

What context window can Granite Code 20B use on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Granite Code 20B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 20B feels slow on MacBook Pro M4 32GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on MacBook Pro M4 32GB as fast as VRAM for Granite Code 20B?

Not always. MacBook Pro M4 32GB 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 32GB See all hardware for Granite Code 20B

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