Can Codestral 22B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

B62Good

Estimated from fit model

Codestral 22B needs ~20.0 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~30 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 20.0 GB, 30.2 tok/s, Runs well

20.0 GB required32.0 GB available

63% VRAM used

Fit status

Runs well

Decode

30.2 tok/s

TTFT

6401 ms

Safe context

33K

Memory

20.0 GB / 32.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodestral 22B 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: 30.2 tok/s decode · 6.4s TTFT (warm) · 76 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	B	Runs well	30.2 tok/s	3491 ms	33K
Coding	B	Runs well	30.2 tok/s	6401 ms	33K
Agentic Coding	B	Runs well	30.2 tok/s	9310 ms	33K
Reasoning	B	Runs well	30.2 tok/s	7564 ms	33K
RAG	B	Runs well	30.2 tok/s	11637 ms	33K

Quantization options

How Codestral 22B (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	B55
Q3_K_S	3	10.8 GB	Low	B56
NVFP4	4	12.3 GB	Medium	B57
Q4_K_M	4	13.4 GB	Medium	B58
Q5_K_M	5	15.8 GB	High	B59
Q6_K	6	18.0 GB	High	B59
Q8_0Best for your GPU	8	23.5 GB	Very High	B59
F16	16	45.1 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral 22B on your machine.

Run

ollama run codestral

Opciones de mejora

Hardware que ejecuta bien Codestral 22B

MacBook Pro M4 Max 48GBOpción económica

48 GB Unified (+16)

Este setup está bastante equilibrado para este modelo.37.4 tok/s decodificación

~$2,499 MSRP

NVIDIA A100 40GBMejor relación calidad-precio

40 GB VRAM (+8)1555 GB/s (+915)

Sube la velocidad estimada de decodificación alrededor de un 229%.99.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 229%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$10,000 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Codestral 22B?

Yes, Radeon AI PRO R9700 32GB can run Codestral 22B with a B grade (Runs well). Expected decode speed: 30.2 tok/s.

How much VRAM does Codestral 22B need?

Codestral 22B (22B parameters) requires approximately 20.0 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 Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Codestral 22B achieves approximately 30.2 tokens per second decode speed with a time-to-first-token of 6401ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Codestral 22B for coding?

For coding workloads, Codestral 22B on Radeon AI PRO R9700 32GB receives a B grade with 30.2 tok/s and 33K context.

What context window can Codestral 22B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, 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.

See all results for Radeon AI PRO R9700 32GB See all hardware for Codestral 22B

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