RTX PRO 6000 Blackwell Workstation Edition 96GBGünstige Wahl1792 GB/s (+992)
B
Raises estimated decode speed by about 208%.114.6 tok/s Dekodierung
Raises estimated decode speed by about 208%.
ca. $9,999 MSRP
Can Codestral 22B run on Mac Studio M2 Ultra 128GB?
YES — Runs Great
B56Good
Estimated from fit model
Codestral 22B needs ~30.6 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~35 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.
Select quantization to explore
Q4_K_M (Medium quality) — 30.6 GB, 37.2 tok/s, Runs well
30.6 GB required92.2 GB available
33% VRAM used
Fit status
Runs well
Decode
37.2 tok/s
TTFT
5209 ms
Safe context
33K
Memory
30.6 GB / 92.2 GB
Memory breakdown
Weights13.4 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsCodestral 22B on Mac Studio M2 Ultra 128GB
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: 37.2 tok/s decode · 5.2s TTFT (warm) · 93 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 | B | Runs well | 34.6 tok/s | 3054 ms | 33K |
| Coding | B | Runs well | 34.6 tok/s | 5599 ms | 33K |
| Agentic Coding | B | Runs well | 34.6 tok/s | 8145 ms | 33K |
| Reasoning | B | Runs well | 34.6 tok/s | 6617 ms | 33K |
| RAG | B | Runs well | 34.6 tok/s | 10181 ms | 33K |
Quantization options
How Codestral 22B (22B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | C50 |
Q3_K_S | 3 | 10.8 GB | Low | C50 |
NVFP4 | 4 | 12.3 GB | Medium | C50 |
Q4_K_M | 4 | 13.4 GB | Medium | C50 |
Q5_K_M | 5 | 15.8 GB | High | C50 |
Q6_K | 6 | 18.0 GB | High | C51 |
Q8_0 | 8 | 23.5 GB | Very High | C51 |
F16Best for your GPU | 16 | 45.1 GB | Maximum | B56 |
Get started
Copy-paste commands to run Codestral 22B on your machine.
Run
ollama run codestralUpgrade-Optionen
Hardware, die Codestral 22B gut ausführt
Frequently asked questions
Can Mac Studio M2 Ultra 128GB run Codestral 22B?
Yes, Mac Studio M2 Ultra 128GB can run Codestral 22B with a B grade (Runs well). Expected decode speed: 34.6 tok/s.
How much VRAM does Codestral 22B need?
Codestral 22B (22B parameters) requires approximately 30.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Codestral 22B?
The recommended quantization for Codestral 22B is Q4_K_M, which balances quality and memory efficiency.
What speed will Codestral 22B run at on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, Codestral 22B achieves approximately 34.6 tokens per second decode speed with a time-to-first-token of 5599ms using Q4_K_M quantization.
Can Mac Studio M2 Ultra 128GB run Codestral 22B for coding?
For coding workloads, Codestral 22B on Mac Studio M2 Ultra 128GB receives a B grade with 34.6 tok/s and 33K context.
What context window can Codestral 22B use on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, Codestral 22B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Codestral 22B?
Not always. Mac Studio M2 Ultra 128GB 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.
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