Radeon Pro W7900 48GB经济之选
864 GB/s (+591)
C
Raises estimated decode speed by about 74%.38 tok/s 解码
Raises estimated decode speed by about 74%.
~$3,999 MSRP
Can Codestral 22B v0.1 run on MacBook Pro M4 Pro 64GB?
YES — Runs Great
C48Usable
Estimated — low-sample bucket· few comparable runs
Codestral 22B v0.1 needs ~23.8 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~22 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 23.8 GB, 21.9 tok/s, Runs well
23.8 GB required46.1 GB available
52% VRAM used
Fit status
Runs well
Decode
21.9 tok/s
TTFT
8828 ms
Safe context
154K
Memory
23.8 GB / 46.1 GB
Memory breakdown
Weights13.4 GB
KV Cache2.6 GB
Runtime0.9 GB
Headroom6.9 GB
See how fast it feels
See how fast it feelsCodestral 22B v0.1 on MacBook Pro M4 Pro 64GB
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: 21.9 tok/s decode · 8.8s TTFT (warm) · 55 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 | 21.9 tok/s | 4815 ms | 154K |
| Coding | C | Runs well | 21.9 tok/s | 8828 ms | 154K |
| Agentic Coding | C | Runs well | 21.9 tok/s | 12841 ms | 154K |
| Reasoning | C | Runs well | 21.9 tok/s | 10433 ms | 154K |
| RAG | C | Runs well | 21.9 tok/s | 16051 ms | 154K |
Quantization options
How Codestral 22B v0.1 (22B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | C43 |
Q3_K_S | 3 | 10.8 GB | Low | C43 |
NVFP4 | 4 | 12.3 GB | Medium | C44 |
Q4_K_M | 4 | 13.4 GB | Medium | C44 |
Q5_K_M | 5 | 15.8 GB | High | C45 |
Q6_K | 6 | 18.0 GB | High | C46 |
Q8_0Best for your GPU | 8 | 23.5 GB | Very High | C47 |
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 start升级选项
能流畅运行 Codestral 22B v0.1 的硬件
Frequently asked questions
Can MacBook Pro M4 Pro 64GB run Codestral 22B v0.1?
Yes, MacBook Pro M4 Pro 64GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 21.9 tok/s.
How much VRAM does Codestral 22B v0.1 need?
Codestral 22B v0.1 (22B parameters) requires approximately 23.8 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 MacBook Pro M4 Pro 64GB?
On MacBook Pro M4 Pro 64GB, Codestral 22B v0.1 achieves approximately 21.9 tokens per second decode speed with a time-to-first-token of 8828ms using Q4_K_M quantization.
Can MacBook Pro M4 Pro 64GB run Codestral 22B v0.1 for coding?
For coding workloads, Codestral 22B v0.1 on MacBook Pro M4 Pro 64GB receives a C grade with 21.9 tok/s and 154K context.
What context window can Codestral 22B v0.1 use on MacBook Pro M4 Pro 64GB?
On MacBook Pro M4 Pro 64GB, Codestral 22B v0.1 can safely use up to 154K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 Pro 64GB as fast as VRAM for Codestral 22B v0.1?
Not always. MacBook Pro M4 Pro 64GB 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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