Can Codestral 21B Pruned i1 run on MacBook Pro M4 Max 64GB?

YES — Runs Great

C49Usable

Estimated from fit model

Codestral 21B Pruned i1 needs ~23.1 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 23.1 GB, 35.2 tok/s, Runs well

23.1 GB required46.1 GB available

50% VRAM used

Fit status

Runs well

Decode

35.2 tok/s

TTFT

5506 ms

Safe context

166K

Memory

23.1 GB / 46.1 GB

Memory breakdown

Weights12.8 GB

KV Cache2.5 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsCodestral 21B Pruned i1 on MacBook Pro M4 Max 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: 35.2 tok/s decode · 5.5s TTFT (warm) · 88 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	35.2 tok/s	3003 ms	166K
Coding	C	Runs well	35.2 tok/s	5506 ms	166K
Agentic Coding	C	Runs well	35.2 tok/s	8008 ms	166K
Reasoning	C	Runs well	35.2 tok/s	6507 ms	166K
RAG	C	Runs well	35.2 tok/s	10010 ms	166K

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	C42
Q3_K_S	3	10.3 GB	Low	C43
NVFP4	4	11.8 GB	Medium	C43
Q4_K_M	4	12.8 GB	Medium	C44
Q5_K_M	5	15.1 GB	High	C44
Q6_K	6	17.2 GB	High	C45
Q8_0Best for your GPU	8	22.5 GB	Very High	C47
F16	16	43.1 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral 21B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-21b-pruned-i1-gguf && lms server start

升级选项

能流畅运行 Codestral 21B Pruned i1 的硬件

RTX PRO 5000 Blackwell 48GB经济之选

1344 GB/s (+798)

Raises estimated decode speed by about 150%.88.1 tok/s 解码

Raises estimated decode speed by about 150%.

~$4,999 MSRP

RTX 6000 Ada 48GB最佳性价比

960 GB/s (+414)

Raises estimated decode speed by about 75%.61.5 tok/s 解码

Raises estimated decode speed by about 75%.

~$6,800 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 64GB run Codestral 21B Pruned i1?

Yes, MacBook Pro M4 Max 64GB can run Codestral 21B Pruned i1 with a C grade (Runs well). Expected decode speed: 35.2 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 23.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 21B Pruned i1?

The recommended quantization for Codestral 21B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 21B Pruned i1 run at on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Codestral 21B Pruned i1 achieves approximately 35.2 tokens per second decode speed with a time-to-first-token of 5506ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 64GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on MacBook Pro M4 Max 64GB receives a C grade with 35.2 tok/s and 166K context.

What context window can Codestral 21B Pruned i1 use on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Codestral 21B Pruned i1 can safely use up to 166K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for Codestral 21B Pruned i1?

Not always. MacBook Pro M4 Max 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.

See all results for MacBook Pro M4 Max 64GB See all hardware for Codestral 21B Pruned i1

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