Can Granite Code 20B run on MacBook Pro M3 24GB?

BARELY — Tight on Memory

B65Good

Estimated from fit model

Granite Code 20B needs ~18.9 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~5 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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) — 18.9 GB, 5.2 tok/s, Very compromised (needs ~1 GB host RAM)

18.9 GB required17.3 GB available

109% VRAM needed

1.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1 GB host RAM)

Decode

5.2 tok/s

TTFT

37307 ms

Safe context

Memory

18.9 GB / 17.3 GB

Offload

10%

Memory breakdown

Weights12.2 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsGranite Code 20B on MacBook Pro M3 24GB
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: 5.2 tok/s decode · 37.3s TTFT (warm) · 13 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload	6.0 tok/s	17542 ms	8K
Coding	B	Very compromised (needs ~1 GB host RAM)	5.2 tok/s	37307 ms	8K
Agentic Coding	F	Too heavy	4.3 tok/s	66207 ms	8K
Reasoning	B	Very compromised (needs ~1 GB host RAM)	5.2 tok/s	44090 ms	8K
RAG	F	Too heavy	4.3 tok/s	82758 ms	8K

Quantization options

How Granite Code 20B (20B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	A81
Q3_K_S	3	9.8 GB	Low	A81
NVFP4	4	11.2 GB	Medium	A80
Q4_K_MBest for your GPU	4	12.2 GB	Medium	A80
Q5_K_M	5	14.4 GB	High	F0
Q6_K	6	16.4 GB	High	F0
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

Opções de upgrade

Hardware que roda bem Granite Code 20B

MacBook Pro M4 32GBOpção econômica

32 GB Unified (+8)120 GB/s (+20)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9.9 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 90%.

~$799 MSRP

Mac mini M4 32GBMelhor custo-benefício

32 GB Unified (+8)120 GB/s (+20)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9.9 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 90%.

~$1,099 MSRP

Mac mini M4 64GBUpgrade Apple

64 GB Unified (+40)120 GB/s (+20)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9.9 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 90%.

~$1,099 MSRP

RTX 5090 Laptop 24GBMaior salto

896 GB/s (+796)

Removes host-memory offload, which is usually the single biggest latency and throughput win.66.6 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 1181%.

Frequently asked questions

Can MacBook Pro M3 24GB run Granite Code 20B?

Yes, MacBook Pro M3 24GB can run Granite Code 20B with a B grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 5.2 tok/s.

How much VRAM does Granite Code 20B need?

Granite Code 20B (20B parameters) requires approximately 18.9 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 M3 24GB?

On MacBook Pro M3 24GB, Granite Code 20B achieves approximately 5.2 tokens per second decode speed with a time-to-first-token of 37307ms using Q4_K_M quantization.

Can MacBook Pro M3 24GB run Granite Code 20B for coding?

For coding workloads, Granite Code 20B on MacBook Pro M3 24GB receives a B grade with 5.2 tok/s and 8K context.

What context window can Granite Code 20B use on MacBook Pro M3 24GB?

On MacBook Pro M3 24GB, 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 M3 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Is unified memory on MacBook Pro M3 24GB as fast as VRAM for Granite Code 20B?

Not always. MacBook Pro M3 24GB 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 M3 24GB See all hardware for Granite Code 20B

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