Can Granite Code 34B run on MacBook Pro M4 Max 48GB?

YES — Tight Fit

A77Great

Estimated from fit model

Granite Code 34B needs ~30.5 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced

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) — 30.5 GB, 31.4 tok/s, Tight fit

30.5 GB required34.6 GB available

88% VRAM used

Fit status

Tight fit

Decode

31.4 tok/s

TTFT

6171 ms

Safe context

Memory

30.5 GB / 34.6 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsGranite Code 34B on MacBook Pro M4 Max 48GB
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: 31.4 tok/s decode · 6.2s TTFT (warm) · 78 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	A	Tight fit	31.4 tok/s	3366 ms	8K
Coding	A	Tight fit	31.4 tok/s	6171 ms	8K
Agentic Coding	A	Runs with offload	31.4 tok/s	8976 ms	8K
Reasoning	A	Tight fit	31.4 tok/s	7293 ms	8K
RAG	A	Runs with offload	31.4 tok/s	11220 ms	8K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on MacBook Pro M4 Max 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	A74
Q3_K_S	3	16.7 GB	Low	A76
NVFP4	4	19.0 GB	Medium	A76
Q4_K_M	4	20.7 GB	Medium	A76
Q5_K_M	5	24.5 GB	High	A75
Q6_KBest for your GPU	6	27.9 GB	High	A75
Q8_0	8	36.4 GB	Very High	F0
F16	16	69.7 GB	Maximum	F0

Get started

Copy-paste commands to run Granite Code 34B on your machine.

Run

ollama run granite-code:34b

Your hardware

More models your MacBook Pro M4 Max 48GB can run

Model	Params	Grade	Decode
Qwen 3.6 35B A3B	35B	S	43.7 tok/s
Qwen 3.5 35B A3B	35B	S	47.5 tok/s
Kimi Linear 48B A3B	48B	A	19.3 tok/s

Frequently asked questions

Can MacBook Pro M4 Max 48GB run Granite Code 34B?

Yes, MacBook Pro M4 Max 48GB can run Granite Code 34B with a A grade (Tight fit). Expected decode speed: 31.4 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 30.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 34B?

The recommended quantization for Granite Code 34B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 34B run at on MacBook Pro M4 Max 48GB?

On MacBook Pro M4 Max 48GB, Granite Code 34B achieves approximately 31.4 tokens per second decode speed with a time-to-first-token of 6171ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 48GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on MacBook Pro M4 Max 48GB receives a A grade with 31.4 tok/s and 8K context.

What context window can Granite Code 34B use on MacBook Pro M4 Max 48GB?

On MacBook Pro M4 Max 48GB, Granite Code 34B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 48GB as fast as VRAM for Granite Code 34B?

Not always. MacBook Pro M4 Max 48GB 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 48GB See all hardware for Granite Code 34B

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<iframe src="https://willitrunai.com/embed/granite-code-34b-on-m4-max-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

31.4 tok/s

3366 ms

Coding

Tight fit

31.4 tok/s

6171 ms

Agentic Coding

Runs with offload

31.4 tok/s

8976 ms

Reasoning

Tight fit

31.4 tok/s

7293 ms

RAG

Runs with offload

31.4 tok/s

11220 ms

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on MacBook Pro M4 Max 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	A74
Q3_K_S	3	16.7 GB	Low	A76
NVFP4	4	19.0 GB	Medium	A76
Q4_K_M	4	20.7 GB	Medium	A76
Q5_K_M	5	24.5 GB	High	A75
Q6_KBest for your GPU	6	27.9 GB	High	A75
Q8_0	8	36.4 GB	Very High	F0
F16	16	69.7 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3.6 35B A3B

35B

43.7 tok/s

Qwen 3.5 35B A3B

35B

47.5 tok/s

Kimi Linear 48B A3B

48B

19.3 tok/s