Can Codestral RAG 19B Pruned i1 run on MacBook Pro M4 32GB?

YES — Runs Great

C49Usable

Estimated — low-sample bucket· few comparable runs

Codestral RAG 19B Pruned i1 needs ~18.2 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 18.2 GB, 9.3 tok/s, Runs well

18.2 GB required23.0 GB available

79% VRAM used

Fit status

Runs well

Decode

9.3 tok/s

TTFT

20776 ms

Safe context

51K

Memory

18.2 GB / 23.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B 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: 9.3 tok/s decode · 20.8s 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	9.3 tok/s	11332 ms	51K
Coding	C	Runs well	9.3 tok/s	20776 ms	51K
Agentic Coding	C	Tight fit	9.3 tok/s	30220 ms	51K
Reasoning	C	Runs well	9.3 tok/s	24554 ms	51K
RAG	C	Tight fit	9.3 tok/s	37775 ms	51K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C47
Q3_K_S	3	9.3 GB	Low	C48
NVFP4	4	10.6 GB	Medium	C49
Q4_K_M	4	11.6 GB	Medium	C50
Q5_K_M	5	13.7 GB	High	C50
Q6_KBest for your GPU	6	15.6 GB	High	C49
Q8_0	8	20.3 GB	Very High	F0
F16	16	38.9 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral RAG 19B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-rag-19b-pruned-i1-gguf && lms server start

Upgrade-Optionen

Hardware, die Codestral RAG 19B Pruned i1 gut ausführt

MacBook Pro M3 Pro 36GBGünstige Wahl

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

This setup is broadly balanced for this model.9.4 tok/s Dekodierung

ca. $1,999 MSRP

MacBook Pro M4 Max 36GBBestes Preis-Leistungs-Verhältnis

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

Raises estimated decode speed by about 209%.28.7 tok/s Dekodierung

Raises estimated decode speed by about 209%.

ca. $2,499 MSRP

MacBook Pro M4 Max 48GBApple-Upgrade

48 GB Unified (+16)546 GB/s (+426)

Raises estimated decode speed by about 287%.36 tok/s Dekodierung

Raises estimated decode speed by about 287%.

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

ca. $2,499 MSRP

Frequently asked questions

Can MacBook Pro M4 32GB run Codestral RAG 19B Pruned i1?

Yes, MacBook Pro M4 32GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 9.3 tok/s.

How much VRAM does Codestral RAG 19B Pruned i1 need?

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 18.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral RAG 19B Pruned i1?

The recommended quantization for Codestral RAG 19B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral RAG 19B Pruned i1 run at on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Codestral RAG 19B Pruned i1 achieves approximately 9.3 tokens per second decode speed with a time-to-first-token of 20776ms using Q4_K_M quantization.

Can MacBook Pro M4 32GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on MacBook Pro M4 32GB receives a C grade with 9.3 tok/s and 51K context.

What context window can Codestral RAG 19B Pruned i1 use on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Codestral RAG 19B Pruned i1 can safely use up to 51K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 32GB as fast as VRAM for Codestral RAG 19B 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.

See all results for MacBook Pro M4 32GB See all hardware for Codestral RAG 19B Pruned i1

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