Can Codestral 21B Pruned i1 run on Radeon AI PRO R9700 32GB?

YES — Runs Great

C51Usable

Estimated from fit model

Codestral 21B Pruned i1 needs ~19.4 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) — 19.4 GB, 29.5 tok/s, Runs well

19.4 GB required32.0 GB available

61% VRAM used

Fit status

Runs well

Decode

29.5 tok/s

TTFT

6568 ms

Safe context

98K

Memory

19.4 GB / 32.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.5 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodestral 21B Pruned i1 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: 29.5 tok/s decode · 6.6s TTFT (warm) · 74 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	29.5 tok/s	3582 ms	98K
Coding	C	Runs well	29.5 tok/s	6568 ms	98K
Agentic Coding	C	Runs well	29.5 tok/s	9553 ms	98K
Reasoning	C	Runs well	29.5 tok/s	7762 ms	98K
RAG	C	Runs well	29.5 tok/s	11942 ms	98K

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	C45
Q3_K_S	3	10.3 GB	Low	C46
NVFP4	4

Get started

Copy-paste commands to run Codestral 21B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-21b-pruned-i1-gguf && lms server start

Upgrade options

Hardware that runs Codestral 21B Pruned i1 well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.35.2 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%.102 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 21B Pruned i1?

Yes, Radeon AI PRO R9700 32GB can run Codestral 21B Pruned i1 with a C grade (Runs well). Expected decode speed: 29.5 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 19.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 21B Pruned i1?

The recommended quantization for Codestral 21B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 21B Pruned i1 run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Codestral 21B Pruned i1 achieves approximately 29.5 tokens per second decode speed with a time-to-first-token of 6568ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on Radeon AI PRO R9700 32GB receives a C grade with 29.5 tok/s and 98K context.

What context window can Codestral 21B Pruned i1 use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Codestral 21B Pruned i1 can safely use up to 98K 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 21B Pruned i1

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