Can Codestral RAG 19B Pruned i1 run on MacBook Pro M1 Pro 32GB?

YES — Runs Great

C50Usable

○Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~18.2 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~11 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, 11.2 tok/s, Runs well

18.2 GB required23.0 GB available

79% VRAM used

Fit status

Runs well

Decode

11.2 tok/s

TTFT

17260 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 M1 Pro 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: 11.2 tok/s decode · 17.3s TTFT (warm) · 28 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	11.2 tok/s	9415 ms	51K
Coding	C	Runs well	11.2 tok/s	17260 ms	51K
Agentic Coding	C	Tight fit	11.2 tok/s	25106 ms	51K
Reasoning	C	Runs well	11.2 tok/s	20398 ms	51K
RAG	C	Tight fit	11.2 tok/s	31382 ms	51K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on MacBook Pro M1 Pro 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