Can Granite Code 34B run on Mac Studio M1 Ultra 128GB?

YES — Runs Great

A73Great

Estimated from fit model

Granite Code 34B needs ~39.1 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 39.1 GB, 23.0 tok/s, Runs well

39.1 GB required92.2 GB available

42% VRAM used

Fit status

Runs well

Decode

23.0 tok/s

TTFT

8424 ms

Safe context

Memory

39.1 GB / 92.2 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsGranite Code 34B on Mac Studio M1 Ultra 128GB
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: 23.0 tok/s decode · 8.4s TTFT (warm) · 58 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	23.0 tok/s	4595 ms	8K
Coding	A	Runs well	23.0 tok/s	8424 ms	8K
Agentic Coding	A	Runs well	23.0 tok/s	12253 ms	8K
Reasoning	A	Runs well	23.0 tok/s	9955 ms	8K
RAG	A	Runs well	23.0 tok/s	15316 ms	8K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	B67
Q3_K_S	3	16.7 GB	Low	B67
NVFP4	4	19.0 GB	Medium	B68
Q4_K_M	4	20.7 GB	Medium	B68
Q5_K_M	5	24.5 GB	High	B69
Q6_K	6	27.9 GB	High	B69
Q8_0	8	36.4 GB	Very High	A71
F16Best for your GPU	16	69.7 GB	Maximum	A75

Get started

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

Run

ollama run granite-code:34b

Your hardware

More models your Mac Studio M1 Ultra 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	6 tok/s
Qwen 3.5 122B A10B	122B	S	27.4 tok/s
Qwen 3.6 35B A3B	35B	S	55.9 tok/s
Qwen 3.5 35B A3B	35B	S	60.8 tok/s
Mistral Small 4 119B	119B	S	29.3 tok/s

Frequently asked questions

Can Mac Studio M1 Ultra 128GB run Granite Code 34B?

Yes, Mac Studio M1 Ultra 128GB can run Granite Code 34B with a A grade (Runs well). Expected decode speed: 23.0 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 39.1 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 Mac Studio M1 Ultra 128GB?

On Mac Studio M1 Ultra 128GB, Granite Code 34B achieves approximately 23.0 tokens per second decode speed with a time-to-first-token of 8424ms using Q4_K_M quantization.

Can Mac Studio M1 Ultra 128GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on Mac Studio M1 Ultra 128GB receives a A grade with 23.0 tok/s and 8K context.

What context window can Granite Code 34B use on Mac Studio M1 Ultra 128GB?

On Mac Studio M1 Ultra 128GB, 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 Mac Studio M1 Ultra 128GB as fast as VRAM for Granite Code 34B?

Not always. Mac Studio M1 Ultra 128GB 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 Studio M1 Ultra 128GB See all hardware for Granite Code 34B

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