Can Codestral RAG 19B Pruned i1 run on Radeon Pro W7800 32GB?

YES — Runs Great

C50Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~17.9 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~29 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, 29.3 tok/s, Runs well

17.9 GB required32.0 GB available

56% VRAM used

Fit status

Runs well

Decode

29.3 tok/s

TTFT

6603 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 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: 29.3 tok/s decode · 6.6s TTFT (warm) · 73 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	29.3 tok/s	3601 ms	117K
Coding	C	Runs well	29.3 tok/s	6603 ms	117K
Agentic Coding	C	Runs well	29.3 tok/s	9604 ms	117K
Reasoning	C	Runs well	29.3 tok/s	7803 ms	117K
RAG	C	Runs well	29.3 tok/s	12005 ms	117K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on Radeon Pro W7800 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

Opciones de mejora

Hardware que ejecuta bien Codestral RAG 19B Pruned i1

MacBook Pro M4 Max 48GBOpción económica

48 GB Unified (+16)

Este setup está bastante equilibrado para este modelo.36 tok/s decodificación

~$2,499 MSRP

RTX PRO 5000 Blackwell 48GBMejor relación calidad-precio

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

Sube la velocidad estimada de decodificación alrededor de un 232%.97.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 232%.

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

~$4,999 MSRP

Frequently asked questions

Can Radeon Pro W7800 32GB run Codestral RAG 19B Pruned i1?

Yes, Radeon Pro W7800 32GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 29.3 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 Pro W7800 32GB?

On Radeon Pro W7800 32GB, Codestral RAG 19B Pruned i1 achieves approximately 29.3 tokens per second decode speed with a time-to-first-token of 6603ms using Q4_K_M quantization.

Can Radeon Pro W7800 32GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on Radeon Pro W7800 32GB receives a C grade with 29.3 tok/s and 117K context.

What context window can Codestral RAG 19B Pruned i1 use on Radeon Pro W7800 32GB?

On Radeon Pro W7800 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 Pro W7800 32GB See all hardware for Codestral RAG 19B Pruned i1

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