Can Granite Code 34B run on MacBook Pro M4 Max 64GB?

YES — Runs Great

A80Great

Estimated from fit model

Granite Code 34B needs ~32.2 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 32.2 GB, 31.4 tok/s, Runs well

32.2 GB required46.1 GB available

70% VRAM used

Fit status

Runs well

Decode

31.4 tok/s

TTFT

6171 ms

Safe context

Memory

32.2 GB / 46.1 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsGranite Code 34B on MacBook Pro M4 Max 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: 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	Runs well	31.4 tok/s	3366 ms	8K
Coding	A	Runs well	31.4 tok/s	6171 ms	8K
Agentic Coding	A	Runs well	31.4 tok/s	8976 ms	8K
Reasoning	A	Runs well	31.4 tok/s	7293 ms	8K
RAG	A	Runs well	31.4 tok/s	11220 ms	8K

Inference speed

Granite Code 34B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite Code 34B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~63 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	62.7	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	31.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	31.4	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	29.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	24.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	23.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	21.7	Heavy offload
RX 7900 XTX 24GB	24 GB	Q4_K_M	20.0	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	19.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	18.6	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	12.5	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	11.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	7.8	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	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 Granite Code 34B (34B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	A71
Q3_K_S	3	16.7 GB	Low	A72
NVFP4	4	19.0 GB	Medium	A73
Q4_K_M	4	20.7 GB	Medium	A74
Q5_K_M	5	24.5 GB	High	A75
Q6_K	6	27.9 GB	High	A75
Q8_0Best for your GPU	8	36.4 GB	Very High	A75
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 64GB 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	21.1 tok/s
Ornith 1.0 35B A3B	35.1B	A	47.4 tok/s

Frequently asked questions

Can MacBook Pro M4 Max 64GB run Granite Code 34B?

Yes, MacBook Pro M4 Max 64GB can run Granite Code 34B with a A grade (Runs well). Expected decode speed: 31.4 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 32.2 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 64GB?

On MacBook Pro M4 Max 64GB, 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 64GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on MacBook Pro M4 Max 64GB 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 64GB?

On MacBook Pro M4 Max 64GB, 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 64GB as fast as VRAM for Granite Code 34B?

Not always. MacBook Pro M4 Max 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 MacBook Pro M4 Max 64GB See all hardware for Granite Code 34B

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