RTX PRO 6000 Blackwell Workstation Edition 96GBOpção econômica1792 GB/s (+992)
C
Raises estimated decode speed by about 224%.112.2 tok/s decodificação
Raises estimated decode speed by about 224%.
~$9,999 MSRP
Can Codestral 22B v0.1 run on Mac Studio M2 Ultra 128GB?
YES — Runs Great
C46Usable
Estimated from fit model
Codestral 22B v0.1 needs ~30.7 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) — 30.7 GB, 34.6 tok/s, Runs well
30.7 GB required92.2 GB available
33% VRAM used
Fit status
Runs well
Decode
34.6 tok/s
TTFT
5599 ms
Safe context
397K
Memory
30.7 GB / 92.2 GB
Memory breakdown
Weights13.4 GB
KV Cache2.6 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsCodestral 22B v0.1 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: 34.6 tok/s decode · 5.6s TTFT (warm) · 86 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 | 34.6 tok/s | 3054 ms | 397K |
| Coding | C | Runs well | 34.6 tok/s | 5599 ms | 397K |
| Agentic Coding | C | Runs well | 34.6 tok/s | 8145 ms | 397K |
| Reasoning | C | Runs well | 34.6 tok/s | 6617 ms | 397K |
| RAG | C | Runs well | 34.6 tok/s | 10181 ms | 397K |
Quantization options
How Codestral 22B v0.1 (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 | D39 |
Q3_K_S | 3 | 10.8 GB | Low | D39 |
NVFP4 | 4 | 12.3 GB | Medium | D40 |
Q4_K_M | 4 | 13.4 GB | Medium | D40 |
Q5_K_M | 5 | 15.8 GB | High | C40 |
Q6_K | 6 | 18.0 GB | High | C40 |
Q8_0 | 8 | 23.5 GB | Very High | C41 |
F16Best for your GPU | 16 | 45.1 GB | Maximum | C46 |
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 startOpções de upgrade
Hardware que roda bem Codestral 22B v0.1
Frequently asked questions
Can Mac Studio M2 Ultra 128GB run Codestral 22B v0.1?
Yes, Mac Studio M2 Ultra 128GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 34.6 tok/s.
How much VRAM does Codestral 22B v0.1 need?
Codestral 22B v0.1 (22B parameters) requires approximately 30.7 GB of memory with Q4_K_M quantization.
What is the best quantization for Codestral 22B v0.1?
The recommended quantization for Codestral 22B v0.1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Codestral 22B v0.1 run at on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, Codestral 22B v0.1 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 v0.1 for coding?
For coding workloads, Codestral 22B v0.1 on Mac Studio M2 Ultra 128GB receives a C grade with 34.6 tok/s and 397K context.
What context window can Codestral 22B v0.1 use on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, Codestral 22B v0.1 can safely use up to 397K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Codestral 22B v0.1?
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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