Can Codestral 22B v0.1 run on Radeon Pro W7800 32GB?

YES — Runs Great

C51Usable

Estimated from fit model

Codestral 22B v0.1 needs ~20.1 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~25 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, 25.3 tok/s, Runs well

20.1 GB required32.0 GB available

63% VRAM used

Fit status

Runs well

Decode

25.3 tok/s

TTFT

7645 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 Pro W7800 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: 25.3 tok/s decode · 7.6s TTFT (warm) · 63 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	25.3 tok/s	4170 ms	90K
Coding	C	Runs well	25.3 tok/s	7645 ms	90K
Agentic Coding	C	Runs well	25.3 tok/s	11120 ms	90K
Reasoning	C	Runs well	25.3 tok/s	9035 ms	90K
RAG	C	Runs well	25.3 tok/s	13900 ms	90K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on Radeon Pro W7800 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 on your machine.

Run

lms load hf-sanctumai--codestral-22b-v0-1-gguf && lms server start

Upgrade options

Hardware that runs Codestral 22B v0.1 well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

Raises estimated decode speed by about 38%.34.8 tok/s decode

Raises estimated decode speed by about 38%.

~$2,499 MSRP

RTX PRO 5000 Blackwell 48GBBest value

48 GB VRAM (+16)1344 GB/s (+768)

Raises estimated decode speed by about 232%.84.1 tok/s decode

Raises estimated decode speed by about 232%.

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

~$4,999 MSRP

Frequently asked questions

Can Radeon Pro W7800 32GB run Codestral 22B v0.1?

Yes, Radeon Pro W7800 32GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 25.3 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 Pro W7800 32GB?

On Radeon Pro W7800 32GB, Codestral 22B v0.1 achieves approximately 25.3 tokens per second decode speed with a time-to-first-token of 7645ms using Q4_K_M quantization.

Can Radeon Pro W7800 32GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on Radeon Pro W7800 32GB receives a C grade with 25.3 tok/s and 90K context.

What context window can Codestral 22B v0.1 use on Radeon Pro W7800 32GB?

On Radeon Pro W7800 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.

See all results for Radeon Pro W7800 32GB See all hardware for Codestral 22B v0.1

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