1555 GB/s (+1009)
C
Raises estimated decode speed by about 180%.97.3 tok/s decode
Raises estimated decode speed by about 180%.
~$10,000 MSRP
Can Codestral 22B v0.1 run on MacBook Pro M4 Max 48GB?
YES — Runs Great
C52Usable
○Estimated from fit model
Codestral 22B v0.1 needs ~22.1 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~35 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
Share:
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.
Select quantization to explore
Q4_K_M (Medium quality) — 22.1 GB, 34.8 tok/s, Runs well
22.1 GB required34.6 GB available
64% VRAM used
Fit status
Runs well
Decode
34.8 tok/s
TTFT
5562 ms
Safe context
93K
Memory
22.1 GB / 34.6 GB
Memory breakdown
Weights13.4 GB
KV Cache2.6 GB
Runtime0.9 GB
Headroom5.2 GB
See how fast it feels
See how fast it feelsCodestral 22B v0.1 on MacBook Pro M4 Max 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: 34.8 tok/s decode · 5.6s TTFT (warm) · 87 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 | 34.8 tok/s | 3034 ms | 93K |
| Coding | C | Runs well | 34.8 tok/s | 5562 ms | 93K |
| Agentic Coding | C | Runs well | 34.8 tok/s | 8090 ms | 93K |
| Reasoning | C | Runs well | 34.8 tok/s | 6573 ms | 93K |
| RAG | C | Runs well | 34.8 tok/s | 10113 ms | 93K |
Quantization options
How Codestral 22B v0.1 (22B params) fits at each quantization level on MacBook Pro M4 Max 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | C45 |
Q3_K_S | 3 | 10.8 GB | Low | C45 |
NVFP4 | 4 | 12.3 GB | Medium | C46 |
Q4_K_M | 4 | 13.4 GB | Medium | C47 |
Q5_K_M | 5 | 15.8 GB | High | C48 |
Q6_K | 6 | 18.0 GB | High | C49 |
Q8_0Best for your GPU | 8 | 23.5 GB | Very High | C48 |
F16 | 16 | 45.1 GB | Maximum | F0 |
Get started
Copy-paste commands to run Codestral 22B v0.1 on your machine.
Run
lms load hf-sanctumai--codestral-22b-v0-1-gguf && lms server startUpgrade options