MacBook Pro M4 Max 48GB经济之选
48 GB Unified (+16)
C
This setup is broadly balanced for this model.34.8 tok/s 解码
~$2,499 MSRP
Can Codestral 22B v0.1 run on Radeon AI PRO R9700 32GB?
YES — Runs Great
C51Usable
Estimated from fit model
Codestral 22B v0.1 needs ~20.1 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~28 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 20.1 GB, 28.1 tok/s, Runs well
20.1 GB required32.0 GB available
63% VRAM used
Fit status
Runs well
Decode
28.1 tok/s
TTFT
6881 ms
Safe context
90K
Memory
20.1 GB / 32.0 GB
Memory breakdown
Weights13.4 GB
KV Cache2.6 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsCodestral 22B v0.1 on Radeon AI PRO R9700 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: 28.1 tok/s decode · 6.9s TTFT (warm) · 70 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 28.1 tok/s | 3753 ms | 90K |
| Coding | C | Runs well | 28.1 tok/s | 6881 ms | 90K |
| Agentic Coding | C | Runs well | 28.1 tok/s | 10008 ms | 90K |
| Reasoning | C | Runs well | 28.1 tok/s | 8132 ms | 90K |
| RAG | C | Runs well | 28.1 tok/s | 12510 ms | 90K |
Quantization options
How Codestral 22B v0.1 (22B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | C45 |
Q3_K_S | 3 | 10.8 GB | Low | C46 |
NVFP4 | 4 | 12.3 GB | Medium | C47 |
Q4_K_M | 4 | 13.4 GB | Medium | C48 |
Q5_K_M | 5 | 15.8 GB | High | C49 |
Q6_K | 6 | 18.0 GB | High | C49 |
Q8_0Best for your GPU | 8 | 23.5 GB | Very High | C49 |
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-lmstudio-community--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 Radeon AI PRO R9700 32GB run Codestral 22B v0.1?
Yes, Radeon AI PRO R9700 32GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 28.1 tok/s.
How much VRAM does Codestral 22B v0.1 need?
Codestral 22B v0.1 (22B parameters) requires approximately 20.1 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 Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, Codestral 22B v0.1 achieves approximately 28.1 tokens per second decode speed with a time-to-first-token of 6881ms using Q4_K_M quantization.
Can Radeon AI PRO R9700 32GB run Codestral 22B v0.1 for coding?
For coding workloads, Codestral 22B v0.1 on Radeon AI PRO R9700 32GB receives a C grade with 28.1 tok/s and 90K context.
What context window can Codestral 22B v0.1 use on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, Codestral 22B v0.1 can safely use up to 90K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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