Will It Run AI

Can Codestral 22B v0.1 run on MacBook Pro M1 Pro 32GB?

YES — Tight Fit

C47Usable
Estimated from fit model

Codestral 22B v0.1 needs ~20.4 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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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, 9.7 tok/s, Tight fit
20.4 GB required23.0 GB available
89% VRAM used

Fit status

Tight fit

Decode

9.7 tok/s

TTFT

19985 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 on MacBook Pro M1 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: 9.7 tok/s decode · 20.0s TTFT (warm) · 24 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

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit9.7 tok/s10901 ms33K
CodingCTight fit9.7 tok/s19985 ms33K
Agentic CodingCRuns with offload9.7 tok/s29070 ms33K
ReasoningCTight fit9.7 tok/s23619 ms33K
RAGCRuns with offload9.7 tok/s36337 ms33K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
8.6 GB
LowC48
Q3_K_S
3
10.8 GB
LowC50
NVFP4
4
12.3 GB
MediumC50
Q4_K_M
4
13.4 GB
MediumC50
Q5_K_M
5
15.8 GB
HighC50
Q6_KBest for your GPU
6
18.0 GB
HighC49
Q8_0
8
23.5 GB
Very HighF0
F16
16
45.1 GB
MaximumF0

Get started

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

Run

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

アップグレードオプション

Codestral 22B v0.1を快適に動かすハードウェア

MacBook Pro M4 Pro 64GBコスパ重視
64 GB Unified (+32)273 GB/s (+73)
C
Raises estimated decode speed by about 126%.21.9 tok/sデコード

Raises estimated decode speed by about 126%.

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

〜$1,599 MSRP

MacBook Pro M3 Pro 36GBコスパ最良
36 GB Unified (+4)
C
This setup is broadly balanced for this model.8.2 tok/sデコード

〜$1,999 MSRP

MacBook Pro M4 Max 36GBAppleアップグレード
36 GB Unified (+4)410 GB/s (+210)
C
Raises estimated decode speed by about 187%.27.8 tok/sデコード

Raises estimated decode speed by about 187%.

〜$2,499 MSRP

Frequently asked questions

Can MacBook Pro M1 Pro 32GB run Codestral 22B v0.1?

Yes, MacBook Pro M1 Pro 32GB can run Codestral 22B v0.1 with a C grade (Tight fit). Expected decode speed: 9.7 tok/s.

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

Codestral 22B v0.1 (22B parameters) requires approximately 20.4 GB of memory with Q4_K_M quantization.

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

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

What speed will Codestral 22B v0.1 run at on MacBook Pro M1 Pro 32GB?

On MacBook Pro M1 Pro 32GB, Codestral 22B v0.1 achieves approximately 9.7 tokens per second decode speed with a time-to-first-token of 19985ms using Q4_K_M quantization.

Can MacBook Pro M1 Pro 32GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on MacBook Pro M1 Pro 32GB receives a C grade with 9.7 tok/s and 33K context.

What context window can Codestral 22B v0.1 use on MacBook Pro M1 Pro 32GB?

On MacBook Pro M1 Pro 32GB, Codestral 22B v0.1 can safely use up to 33K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M1 Pro 32GB as fast as VRAM for Codestral 22B v0.1?

Not always. MacBook Pro M1 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.

See all results for MacBook Pro M1 Pro 32GBSee all hardware for Codestral 22B v0.1
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