Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for Codestral RAG 19B Pruned i1?

Not always. Mac Studio M2 Ultra 64GB 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 RAG 19B Pruned i1 run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

C49Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~21.6 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~40 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) — 21.6 GB, 40.0 tok/s, Runs well

21.6 GB required46.1 GB available

47% VRAM used

Fit status

Runs well

Decode

40.0 tok/s

TTFT

4836 ms

Safe context

192K

Memory

21.6 GB / 46.1 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on Mac Studio M2 Ultra 64GB
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: 40.0 tok/s decode · 4.8s TTFT (warm) · 100 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	40.0 tok/s	2638 ms	192K
Coding	C	Runs well	40.0 tok/s	4836 ms	192K
Agentic Coding	C	Runs well	40.0 tok/s	7034 ms	192K
Reasoning	C	Runs well	40.0 tok/s	5715 ms	192K
RAG	C	Runs well	40.0 tok/s	8792 ms	192K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C42
Q3_K_S	3	9.3 GB	Low	C42
NVFP4	4

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 options

Hardware that runs Codestral RAG 19B Pruned i1 well

RTX PRO 5000 Blackwell 48GBBudget pick

1344 GB/s (+544)

Raises estimated decode speed by about 144%.97.4 tok/s decode

Raises estimated decode speed by about 144%.

~$4,999 MSRP

RTX 6000 Ada 48GBBest value

960 GB/s (+160)

Raises estimated decode speed by about 70%.67.9 tok/s decode

Raises estimated decode speed by about 70%.

~$6,800 MSRP

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run Codestral RAG 19B Pruned i1?

Yes, Mac Studio M2 Ultra 64GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 40.0 tok/s.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 21.6 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 M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Codestral RAG 19B Pruned i1 achieves approximately 40.0 tokens per second decode speed with a time-to-first-token of 4836ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on Mac Studio M2 Ultra 64GB receives a C grade with 40.0 tok/s and 192K context.

What context window can Codestral RAG 19B Pruned i1 use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Codestral RAG 19B Pruned i1 can safely use up to 192K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Mac Studio M2 Ultra 64GB See all hardware for Codestral RAG 19B Pruned i1

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