Can Codestral 22B v0.1 IMat run on Radeon AI PRO R9700 32GB?

YES — Runs Great

C51Usable

Estimated from fit model

Codestral 22B v0.1 IMat 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.

Capabilities:

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 IMat 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 IMat (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

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

Upgrade options

Hardware that runs Codestral 22B v0.1 IMat well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.34.8 tok/s decode

~$2,499 MSRP

NVIDIA A100 40GBBest value

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

Raises estimated decode speed by about 246%.97.3 tok/s decode

Raises estimated decode speed by about 246%.

Adds memory headroom for longer context windows and future model growth.

~$10,000 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Codestral 22B v0.1 IMat?

Yes, Radeon AI PRO R9700 32GB can run Codestral 22B v0.1 IMat with a C grade (Runs well). Expected decode speed: 28.1 tok/s.

How much VRAM does Codestral 22B v0.1 IMat need?

Codestral 22B v0.1 IMat (22B parameters) requires approximately 20.1 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 Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Codestral 22B v0.1 IMat 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 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat 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 IMat use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Codestral 22B v0.1 IMat can safely use up to 90K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for Codestral 22B v0.1 IMat

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