Can Codestral 22B run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

C55Usable

Estimated from fit model

Codestral 22B needs ~44.4 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~45 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) — 44.4 GB, 44.6 tok/s, Runs well

44.4 GB required184.3 GB available

24% VRAM used

Fit status

Runs well

Decode

44.6 tok/s

TTFT

4340 ms

Safe context

33K

Memory

44.4 GB / 184.3 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsCodestral 22B on Mac Studio M3 Ultra 256GB
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: 44.6 tok/s decode · 4.3s TTFT (warm) · 112 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	44.6 tok/s	2367 ms	33K
Coding	C	Runs well	44.6 tok/s	4340 ms	33K
Agentic Coding	B	Runs well	44.6 tok/s	6312 ms	33K
Reasoning	C	Runs well	44.6 tok/s	5129 ms	33K
RAG	B	Runs well	44.6 tok/s	7890 ms	33K

Quantization options

How Codestral 22B (22B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C47
Q3_K_S	3	10.8 GB	Low	C47
NVFP4	4	12.3 GB	Medium	C47
Q4_K_M	4	13.4 GB	Medium	C47
Q5_K_M	5	15.8 GB	High	C47
Q6_K	6	18.0 GB	High	C47
Q8_0	8	23.5 GB	Very High	C48
F16Best for your GPU	16	45.1 GB	Maximum	C51

Get started

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

Run

ollama run codestral

Opciones de mejora

Hardware que ejecuta bien Codestral 22B

AMD Instinct MI350X 288GBOpción económica

288 GB VRAM (+32)8000 GB/s (+7181)

Sube la velocidad estimada de decodificación alrededor de un 591%.308 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 591%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$8,000 MSRP

AMD Instinct MI300X 192GBMejor relación calidad-precio

5300 GB/s (+4481)

Sube la velocidad estimada de decodificación alrededor de un 591%.308 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 591%.

~$15,000 MSRP

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run Codestral 22B?

Yes, Mac Studio M3 Ultra 256GB can run Codestral 22B with a C grade (Runs well). Expected decode speed: 44.6 tok/s.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 44.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B?

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

What speed will Codestral 22B run at on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Codestral 22B achieves approximately 44.6 tokens per second decode speed with a time-to-first-token of 4340ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on Mac Studio M3 Ultra 256GB receives a C grade with 44.6 tok/s and 33K context.

What context window can Codestral 22B use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Codestral 22B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for Codestral 22B?

Not always. Mac Studio M3 Ultra 256GB 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 M3 Ultra 256GB See all hardware for Codestral 22B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/codestral-22b-on-m3-ultra-256gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: