Can Codestral RAG 19B Pruned i1 run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

C44Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~42.4 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 42.4 GB, 48.1 tok/s, Runs well

42.4 GB required184.3 GB available

23% VRAM used

Fit status

Runs well

Decode

48.1 tok/s

TTFT

4029 ms

Safe context

1.0M

Memory

42.4 GB / 184.3 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on Mac Studio M3 Ultra 256GB
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: 48.1 tok/s decode · 4.0s TTFT (warm) · 120 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	48.1 tok/s	2198 ms	1.0M
Coding	C	Runs well	48.1 tok/s	4029 ms	1.0M
Agentic Coding	C	Runs well	48.1 tok/s	5860 ms	1.0M
Reasoning	C	Runs well	48.1 tok/s	4762 ms	1.0M
RAG	C	Runs well	48.1 tok/s	7325 ms	1.0M

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	D37
Q3_K_S	3	9.3 GB	Low	D37
NVFP4	4	10.6 GB	Medium	D37
Q4_K_M	4	11.6 GB	Medium	D37
Q5_K_M	5	13.7 GB	High	D37
Q6_K	6	15.6 GB	High	D37
Q8_0	8	20.3 GB	Very High	D37
F16Best for your GPU	16	38.9 GB	Maximum	D39

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

AMD Instinct MI350X 288GBGünstige Wahl

288 GB VRAM (+32)8000 GB/s (+7181)

Raises estimated decode speed by about 453%.266 tok/s Dekodierung

Raises estimated decode speed by about 453%.

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

ca. $8,000 MSRP

AMD Instinct MI300X 192GBBestes Preis-Leistungs-Verhältnis

5300 GB/s (+4481)

Raises estimated decode speed by about 453%.266 tok/s Dekodierung

Raises estimated decode speed by about 453%.

ca. $15,000 MSRP

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run Codestral RAG 19B Pruned i1?

Yes, Mac Studio M3 Ultra 256GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 48.1 tok/s.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 42.4 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 Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Codestral RAG 19B Pruned i1 achieves approximately 48.1 tokens per second decode speed with a time-to-first-token of 4029ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on Mac Studio M3 Ultra 256GB receives a C grade with 48.1 tok/s and 1.0M context.

What context window can Codestral RAG 19B Pruned i1 use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Codestral RAG 19B Pruned i1 can safely use up to 1.0M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for Codestral RAG 19B Pruned i1?

Not always. Mac Studio M3 Ultra 256GB 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 Mac Studio M3 Ultra 256GB See all hardware for Codestral RAG 19B Pruned i1

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