Is unified memory on MacBook Pro M3 24GB as fast as VRAM for Codestral 21B Pruned i1?

Not always. MacBook Pro M3 24GB 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 M3 24GB?

Q: What should I upgrade first if Codestral 21B Pruned i1 feels slow on MacBook Pro M3 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

D34Poor

Estimated from fit model

Codestral 21B Pruned i1 needs ~18.8 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~5 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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) — 18.8 GB, 4.6 tok/s, Very compromised (needs ~1 GB host RAM)

18.8 GB required17.3 GB available

109% VRAM needed

1.5 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1 GB host RAM)

Decode

4.6 tok/s

TTFT

41983 ms

Safe context

Memory

18.8 GB / 17.3 GB

Offload

10%

Memory breakdown

Weights12.8 GB

KV Cache2.5 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsCodestral 21B Pruned i1 on MacBook Pro M3 24GB
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: 4.6 tok/s decode · 42.0s TTFT (warm) · 12 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0.2 GB host RAM)	5.1 tok/s	20585 ms	6K
Coding	D	Very compromised	4.6 tok/s	41983 ms	6K
Agentic Coding	F	Too heavy	3.9 tok/s	71705 ms	6K
Reasoning	D	Very compromised (needs ~1 GB host RAM)	4.6 tok/s	49616 ms	6K
RAG	F	Too heavy	3.9 tok/s	89631 ms

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	C51
Q3_K_S	3	10.3 GB	Low	C50
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 32GBBudget pick

32 GB Unified (+8)120 GB/s (+20)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9.1 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 98%.

~$799 MSRP

Mac mini M4 32GBBest value

32 GB Unified (+8)120 GB/s (+20)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9.1 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 98%.

~$1,099 MSRP

Mac mini M4 64GBApple upgrade

64 GB Unified (+40)120 GB/s (+20)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9.1 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 98%.

~$1,099 MSRP

RTX 5090 Laptop 24GBBiggest leap

896 GB/s (+796)

Removes host-memory offload, which is usually the single biggest latency and throughput win.58.8 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 1178%.

Frequently asked questions

Can MacBook Pro M3 24GB run Codestral 21B Pruned i1?

Yes, MacBook Pro M3 24GB can run Codestral 21B Pruned i1 with a D grade (Very compromised). Expected decode speed: 4.6 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 18.8 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 M3 24GB?

On MacBook Pro M3 24GB, Codestral 21B Pruned i1 achieves approximately 4.6 tokens per second decode speed with a time-to-first-token of 41983ms using Q4_K_M quantization.

Can MacBook Pro M3 24GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on MacBook Pro M3 24GB receives a D grade with 4.6 tok/s and 6K context.

What context window can Codestral 21B Pruned i1 use on MacBook Pro M3 24GB?

On MacBook Pro M3 24GB, Codestral 21B Pruned i1 can safely use up to 6K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M3 24GB See all hardware for Codestral 21B Pruned i1

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