Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for Codestral 22B v0.1 IMat?

Not always. MacBook Pro M2 Pro 32GB 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 MacBook Pro M2 Pro 32GB?

YES — Tight Fit

C47Usable

Estimated from fit model

Codestral 22B v0.1 IMat needs ~20.4 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 20.4 GB, 10.4 tok/s, Tight fit

20.4 GB required23.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

10.4 tok/s

TTFT

18558 ms

Safe context

33K

Memory

20.4 GB / 23.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 IMat on MacBook Pro M2 Pro 32GB
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: 10.4 tok/s decode · 18.6s TTFT (warm) · 26 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	Tight fit	10.4 tok/s	10123 ms	33K
Coding	C	Tight fit	10.4 tok/s	18558 ms	33K
Agentic Coding	C	Runs with offload	10.4 tok/s	26993 ms	33K
Reasoning	C	Tight fit	10.4 tok/s	21932 ms	33K
RAG	C	Runs with offload	10.4 tok/s	33742 ms	33K

Quantization options

How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C48
Q3_K_S	3	10.8 GB	Low	C50
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

MacBook Pro M4 Pro 64GBBudget pick

64 GB Unified (+32)273 GB/s (+73)

Raises estimated decode speed by about 111%.21.9 tok/s decode

Raises estimated decode speed by about 111%.

Adds memory headroom for longer context windows and future model growth.

~$1,599 MSRP

MacBook Pro M3 Pro 36GBBest value

36 GB Unified (+4)

This setup is broadly balanced for this model.8.2 tok/s decode

~$1,999 MSRP

MacBook Pro M4 Max 36GBApple upgrade

36 GB Unified (+4)410 GB/s (+210)

Raises estimated decode speed by about 167%.27.8 tok/s decode

Raises estimated decode speed by about 167%.

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run Codestral 22B v0.1 IMat?

Yes, MacBook Pro M2 Pro 32GB can run Codestral 22B v0.1 IMat with a C grade (Tight fit). Expected decode speed: 10.4 tok/s.

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

Codestral 22B v0.1 IMat (22B parameters) requires approximately 20.4 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 MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Codestral 22B v0.1 IMat achieves approximately 10.4 tokens per second decode speed with a time-to-first-token of 18558ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 32GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on MacBook Pro M2 Pro 32GB receives a C grade with 10.4 tok/s and 33K context.

What context window can Codestral 22B v0.1 IMat use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Codestral 22B v0.1 IMat can safely use up to 33K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M2 Pro 32GB See all hardware for Codestral 22B v0.1 IMat

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