Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for Codestral 22B v0.1 IMat?

Not always. Mac Studio M2 Ultra 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.

Can Codestral 22B v0.1 IMat run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

C49Usable

Estimated from fit model

Codestral 22B v0.1 IMat needs ~23.8 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~35 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) — 23.8 GB, 34.6 tok/s, Runs well

23.8 GB required46.1 GB available

52% VRAM used

Fit status

Runs well

Decode

34.6 tok/s

TTFT

5599 ms

Safe context

154K

Memory

23.8 GB / 46.1 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 IMat on Mac Studio M2 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: 34.6 tok/s decode · 5.6s TTFT (warm) · 86 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	34.6 tok/s	3054 ms	154K
Coding	C	Runs well	34.6 tok/s	5599 ms	154K
Agentic Coding	C	Runs well	34.6 tok/s	8145 ms	154K
Reasoning	C	Runs well	34.6 tok/s	6617 ms	154K
RAG	C	Runs well	34.6 tok/s	10181 ms	154K

Quantization options

How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C42
Q3_K_S	3	10.8 GB	Low	C43
NVFP4	4

Get started

Copy-paste commands to run Codestral 22B v0.1 IMat on your machine.

Run

lms load hf-legraphista--codestral-22b-v0-1-imat-gguf && lms server start

Upgrade options

Hardware that runs Codestral 22B v0.1 IMat well

RTX PRO 5000 Blackwell 48GBBudget pick

1344 GB/s (+544)

Raises estimated decode speed by about 143%.84.1 tok/s decode

Raises estimated decode speed by about 143%.

~$4,999 MSRP

RTX 6000 Ada 48GBBest value

960 GB/s (+160)

Raises estimated decode speed by about 70%.58.7 tok/s decode

Raises estimated decode speed by about 70%.

~$6,800 MSRP

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run Codestral 22B v0.1 IMat?

Yes, Mac Studio M2 Ultra 64GB can run Codestral 22B v0.1 IMat with a C grade (Runs well). Expected decode speed: 34.6 tok/s.

How much VRAM does Codestral 22B v0.1 IMat need?

Codestral 22B v0.1 IMat (22B parameters) requires approximately 23.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1 IMat?

The recommended quantization for Codestral 22B v0.1 IMat is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 IMat run at on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Codestral 22B v0.1 IMat achieves approximately 34.6 tokens per second decode speed with a time-to-first-token of 5599ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on Mac Studio M2 Ultra 64GB receives a C grade with 34.6 tok/s and 154K context.

What context window can Codestral 22B v0.1 IMat use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Codestral 22B v0.1 IMat can safely use up to 154K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Mac Studio M2 Ultra 64GB See all hardware for Codestral 22B v0.1 IMat

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