MacBook Pro M3 Pro 36GB经济之选
36 GB Unified (+4)
C
This setup is broadly balanced for this model.8.2 tok/s 解码
~$1,999 MSRP
Can Codestral 22B v0.1 IMat run on MacBook Pro M2 Max 32GB?
YES — Tight Fit
C48Usable
Estimated from fit model
Codestral 22B v0.1 IMat needs ~20.4 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~17 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 20.4 GB, 17.3 tok/s, Tight fit
20.4 GB required23.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
17.3 tok/s
TTFT
11199 ms
Safe context
33K
Memory
20.4 GB / 23.0 GB
Memory breakdown
Weights13.4 GB
KV Cache2.6 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsCodestral 22B v0.1 IMat on MacBook Pro M2 Max 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: 17.3 tok/s decode · 11.2s TTFT (warm) · 43 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 | Tight fit | 17.3 tok/s | 6108 ms | 33K |
| Coding | C | Tight fit | 17.3 tok/s | 11199 ms | 33K |
| Agentic Coding | C | Runs with offload | 17.3 tok/s | 16289 ms | 33K |
| Reasoning | C | Tight fit | 17.3 tok/s | 13235 ms | 33K |
| RAG | C | Runs with offload | 17.3 tok/s | 20361 ms | 33K |
Quantization options
How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | C48 |
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 | C49 |
Q6_KBest for your GPU | 6 | 18.0 GB | High | C49 |
Q8_0 | 8 | 23.5 GB | Very High | F0 |
F16 | 16 | 45.1 GB | Maximum | F0 |
Get started
Copy-paste commands to run Codestral 22B v0.1 IMat on your machine.
Run
lms load hf-legraphista--codestral-22b-v0-1-imat-gguf && lms server start升级选项
能流畅运行 Codestral 22B v0.1 IMat 的硬件
MacBook Pro M4 Max 36GB最佳性价比
36 GB Unified (+4)410 GB/s (+10)
C
Raises estimated decode speed by about 61%.27.8 tok/s 解码
Raises estimated decode speed by about 61%.
~$2,499 MSRP
MacBook Pro M4 Max 48GBApple 升级
48 GB Unified (+16)546 GB/s (+146)
C
Raises estimated decode speed by about 101%.34.8 tok/s 解码
Raises estimated decode speed by about 101%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Frequently asked questions
Can MacBook Pro M2 Max 32GB run Codestral 22B v0.1 IMat?
Yes, MacBook Pro M2 Max 32GB can run Codestral 22B v0.1 IMat with a C grade (Tight fit). Expected decode speed: 17.3 tok/s.
How much VRAM does Codestral 22B v0.1 IMat need?
Codestral 22B v0.1 IMat (22B parameters) requires approximately 20.4 GB of memory with Q4_K_M quantization.
What is the best quantization for Codestral 22B v0.1 IMat?
The recommended quantization for Codestral 22B v0.1 IMat is Q4_K_M, which balances quality and memory efficiency.
What speed will Codestral 22B v0.1 IMat run at on MacBook Pro M2 Max 32GB?
On MacBook Pro M2 Max 32GB, Codestral 22B v0.1 IMat achieves approximately 17.3 tokens per second decode speed with a time-to-first-token of 11199ms using Q4_K_M quantization.
Can MacBook Pro M2 Max 32GB run Codestral 22B v0.1 IMat for coding?
For coding workloads, Codestral 22B v0.1 IMat on MacBook Pro M2 Max 32GB receives a C grade with 17.3 tok/s and 33K context.
What context window can Codestral 22B v0.1 IMat use on MacBook Pro M2 Max 32GB?
On MacBook Pro M2 Max 32GB, Codestral 22B v0.1 IMat can safely use up to 33K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for Codestral 22B v0.1 IMat?
Not always. MacBook Pro M2 Max 32GB 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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