Can Codestral RAG 19B Pruned i1 run on MacBook Pro M4 Pro 48GB?

YES — Runs Great

C48Usable

Estimated — low-sample bucket· few comparable runs

Codestral RAG 19B Pruned i1 needs ~19.9 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 19.9 GB, 22.7 tok/s, Runs well

19.9 GB required34.6 GB available

58% VRAM used

Fit status

Runs well

Decode

22.7 tok/s

TTFT

8539 ms

Safe context

121K

Memory

19.9 GB / 34.6 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on MacBook Pro M4 Pro 48GB
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: 22.7 tok/s decode · 8.5s TTFT (warm) · 57 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	18.1 tok/s	5822 ms	121K
Coding	C	Runs well	18.1 tok/s	10674 ms	121K
Agentic Coding	C	Runs well	18.1 tok/s	15526 ms	121K
Reasoning	C	Runs well	18.1 tok/s	12615 ms	121K
RAG	C	Runs well	18.1 tok/s	19408 ms	121K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C44
Q3_K_S	3	9.3 GB	Low	C44
NVFP4	4	10.6 GB	Medium	C45
Q4_K_M	4	11.6 GB	Medium	C45
Q5_K_M	5	13.7 GB	High	C46
Q6_K	6	15.6 GB	High	C47
Q8_0Best for your GPU	8	20.3 GB	Very High	C49
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

Opciones de mejora

Hardware que ejecuta bien Codestral RAG 19B Pruned i1

RTX PRO 5000 Blackwell 48GBOpción económica

1344 GB/s (+1071)

Sube la velocidad estimada de decodificación alrededor de un 329%.97.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 329%.

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

~$4,999 MSRP

NVIDIA A100 40GBMejor relación calidad-precio

1555 GB/s (+1282)

Sube la velocidad estimada de decodificación alrededor de un 396%.112.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 396%.

~$10,000 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 48GB run Codestral RAG 19B Pruned i1?

Yes, MacBook Pro M4 Pro 48GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 18.1 tok/s.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 19.9 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 Pro 48GB?

On MacBook Pro M4 Pro 48GB, Codestral RAG 19B Pruned i1 achieves approximately 18.1 tokens per second decode speed with a time-to-first-token of 10674ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 48GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on MacBook Pro M4 Pro 48GB receives a C grade with 18.1 tok/s and 121K context.

What context window can Codestral RAG 19B Pruned i1 use on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, Codestral RAG 19B Pruned i1 can safely use up to 121K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for Codestral RAG 19B Pruned i1?

Not always. MacBook Pro M4 Pro 48GB 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 Pro 48GB See all hardware for Codestral RAG 19B Pruned i1

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