Can granite 8b code instruct 4k run on Mac Studio M1 Ultra 64GB?

YES — Runs Great

C48Usable

○Estimated from fit model

granite 8b code instruct 4k needs ~13.6 GB VRAM. Mac Studio M1 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~90 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) — 13.6 GB, 90.2 tok/s, Runs well

13.6 GB required46.1 GB available

30% VRAM used

Fit status

Runs well

Decode

90.2 tok/s

TTFT

2147 ms

Safe context

570K

Memory

13.6 GB / 46.1 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on Mac Studio M1 Ultra 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: 90.2 tok/s decode · 2.1s TTFT (warm) · 225 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	C	Runs well	90.2 tok/s	1171 ms	570K
Coding	C	Runs well	90.2 tok/s	2147 ms	570K
Agentic Coding	C	Runs well	90.2 tok/s	3123 ms	570K
Reasoning	C	Runs well	90.2 tok/s	2538 ms	570K
RAG	C	Runs well	90.2 tok/s	3904 ms	570K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on Mac Studio M1 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C41
Q3_K_S	3	3.9 GB	Low	C42
NVFP4	4	4.5 GB	Medium	C42
Q4_K_M	4	4.9 GB	Medium	C42
Q5_K_M	5	5.8 GB	High	C42
Q6_K	6	6.6 GB	High	C42
Q8_0	8	8.6 GB	Very High	C43
F16Best for your GPU	16	16.4 GB	Maximum	C45

Get started

Copy-paste commands to run granite 8b code instruct 4k on your machine.

Run

lms load hf-ibm-granite--granite-8b-code-instruct-4k-gguf && lms server start

Frequently asked questions

See all results for Mac Studio M1 Ultra 64GB See all hardware for granite 8b code instruct 4k

URL: https://willitrunai.com/can-run/hf-ibm-granite--granite-8b-code-instruct-4k-gguf-on-m1-ultra-64gb

⇱ granite 8b code instruct 4k on Mac Studio M1 Ultra 64GB? YES