Can Codestral RAG 19B Pruned i1 run on Radeon AI PRO R9700 32GB?

YES — Runs Great

C50Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~17.9 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~33 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) — 17.9 GB, 32.6 tok/s, Runs well

17.9 GB required32.0 GB available

56% VRAM used

Fit status

Runs well

Decode

32.6 tok/s

TTFT

5942 ms

Safe context

117K

Memory

17.9 GB / 32.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B 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: 32.6 tok/s decode · 5.9s TTFT (warm) · 81 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	32.6 tok/s	3241 ms	117K
Coding	C	Runs well	32.6 tok/s	5942 ms	117K
Agentic Coding	C	Runs well	32.6 tok/s	8643 ms	117K
Reasoning	C	Runs well	32.6 tok/s	7023 ms	117K
RAG	C	Runs well	32.6 tok/s	10804 ms	117K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C44
Q3_K_S	3	9.3 GB	Low	C45
NVFP4	4	10.6 GB	Medium	C46
Q4_K_M	4	11.6 GB	Medium	C46
Q5_K_M	5	13.7 GB	High	C47
Q6_K	6	15.6 GB	High	C48
Q8_0Best for your GPU	8	20.3 GB	Very High	C49
F16	16	38.9 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral RAG 19B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-rag-19b-pruned-i1-gguf && lms server start

Upgrade options

Hardware that runs Codestral RAG 19B Pruned i1 well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.36 tok/s decode

~$2,499 MSRP

RTX PRO 5000 Blackwell 48GBBest value

48 GB VRAM (+16)1344 GB/s (+704)

Raises estimated decode speed by about 199%.97.4 tok/s decode

Raises estimated decode speed by about 199%.

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

~$4,999 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Codestral RAG 19B Pruned i1?

Yes, Radeon AI PRO R9700 32GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 32.6 tok/s.

How much VRAM does Codestral RAG 19B Pruned i1 need?

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 17.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral RAG 19B Pruned i1?

The recommended quantization for Codestral RAG 19B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral RAG 19B Pruned i1 run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Codestral RAG 19B Pruned i1 achieves approximately 32.6 tokens per second decode speed with a time-to-first-token of 5942ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on Radeon AI PRO R9700 32GB receives a C grade with 32.6 tok/s and 117K context.

What context window can Codestral RAG 19B Pruned i1 use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Codestral RAG 19B Pruned i1 can safely use up to 117K 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 RAG 19B Pruned i1

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<iframe src="https://willitrunai.com/embed/hf-mradermacher--codestral-rag-19b-pruned-i1-gguf-on-radeon-ai-pro-r9700-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

32.6 tok/s

3241 ms

117K

Coding

Runs well

32.6 tok/s

5942 ms

117K

Agentic Coding

Runs well

32.6 tok/s

8643 ms

117K

Reasoning

Runs well

32.6 tok/s

7023 ms

117K

RAG

Runs well

32.6 tok/s

10804 ms

117K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C44
Q3_K_S	3	9.3 GB	Low	C45
NVFP4	4	10.6 GB	Medium	C46
Q4_K_M	4	11.6 GB	Medium	C46
Q5_K_M	5	13.7 GB	High	C47
Q6_K	6	15.6 GB	High	C48
Q8_0Best for your GPU	8	20.3 GB	Very High	C49
F16	16	38.9 GB	Maximum	F0