Is unified memory on MacBook Pro M4 32GB as fast as VRAM for Codestral 21B Pruned i1?

Not always. MacBook Pro M4 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 21B Pruned i1 run on MacBook Pro M4 32GB?

YES — Tight Fit

C46Usable

Estimated — low-sample bucket· few comparable runs

Codestral 21B Pruned i1 needs ~19.6 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 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) — 19.6 GB, 9.1 tok/s, Tight fit

19.6 GB required23.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

9.1 tok/s

TTFT

21262 ms

Safe context

38K

Memory

19.6 GB / 23.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.5 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsCodestral 21B Pruned i1 on MacBook Pro M4 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: 9.1 tok/s decode · 21.3s TTFT (warm) · 23 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	9.1 tok/s	11598 ms	38K
Coding	C	Tight fit	9.1 tok/s	21262 ms	38K
Agentic Coding	C	Runs with offload	9.1 tok/s	30927 ms	38K
Reasoning	C	Tight fit	9.1 tok/s	25128 ms	38K
RAG	C	Runs with offload	9.1 tok/s	38658 ms	38K

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	C48
Q3_K_S	3	10.3 GB	Low	C49
NVFP4	4

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

Upgrade options

Hardware that runs Codestral 21B Pruned i1 well

MacBook Pro M4 Pro 64GBBudget pick

64 GB Unified (+32)273 GB/s (+153)

Raises estimated decode speed by about 144%.22.2 tok/s decode

Raises estimated decode speed by about 144%.

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

~$1,599 MSRP

MacBook Pro M3 Pro 36GBBest value

36 GB Unified (+4)150 GB/s (+30)

This setup is broadly balanced for this model.8.5 tok/s decode

~$1,999 MSRP

MacBook Pro M4 Max 36GBApple upgrade

36 GB Unified (+4)410 GB/s (+290)

Raises estimated decode speed by about 208%.28 tok/s decode

Raises estimated decode speed by about 208%.

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M4 32GB run Codestral 21B Pruned i1?

Yes, MacBook Pro M4 32GB can run Codestral 21B Pruned i1 with a C grade (Tight fit). Expected decode speed: 9.1 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 19.6 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 32GB?

On MacBook Pro M4 32GB, Codestral 21B Pruned i1 achieves approximately 9.1 tokens per second decode speed with a time-to-first-token of 21262ms using Q4_K_M quantization.

Can MacBook Pro M4 32GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on MacBook Pro M4 32GB receives a C grade with 9.1 tok/s and 38K context.

What context window can Codestral 21B Pruned i1 use on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Codestral 21B Pruned i1 can safely use up to 38K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 32GB See all hardware for Codestral 21B Pruned i1

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