Can Codestral RAG 19B Pruned i1 run on NVIDIA L20 48GB?

YES — Runs Great

C48Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~19.8 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 19.8 GB, 54.4 tok/s, Runs well

19.8 GB required48.0 GB available

41% VRAM used

Fit status

Runs well

Decode

54.4 tok/s

TTFT

3558 ms

Safe context

219K

Memory

19.8 GB / 48.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on NVIDIA L20 48GB
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: 54.4 tok/s decode · 3.6s TTFT (warm) · 136 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	54.4 tok/s	1940 ms	219K
Coding	C	Runs well	54.4 tok/s	3558 ms	219K
Agentic Coding	C	Runs well	54.4 tok/s	5175 ms	219K
Reasoning	C	Runs well	54.4 tok/s	4204 ms	219K
RAG	C	Runs well	54.4 tok/s	6468 ms	219K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C42
Q3_K_S	3	9.3 GB	Low	C42
NVFP4	4	10.6 GB	Medium	C43
Q4_K_M	4	11.6 GB	Medium	C43
Q5_K_M	5	13.7 GB	High	C43
Q6_K	6	15.6 GB	High	C44
Q8_0	8	20.3 GB	Very High	C46
F16Best for your GPU	16	38.9 GB	Maximum	C47

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

AMD Instinct MI210 64GBOpción económica

64 GB VRAM (+16)1638 GB/s (+774)

Sube la velocidad estimada de decodificación alrededor de un 77%.96.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 77%.

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

~$10,000 MSRP

Frequently asked questions

Can NVIDIA L20 48GB run Codestral RAG 19B Pruned i1?

Yes, NVIDIA L20 48GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 54.4 tok/s.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 19.8 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 NVIDIA L20 48GB?

On NVIDIA L20 48GB, Codestral RAG 19B Pruned i1 achieves approximately 54.4 tokens per second decode speed with a time-to-first-token of 3558ms using Q4_K_M quantization.

Can NVIDIA L20 48GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on NVIDIA L20 48GB receives a C grade with 54.4 tok/s and 219K context.

What context window can Codestral RAG 19B Pruned i1 use on NVIDIA L20 48GB?

On NVIDIA L20 48GB, Codestral RAG 19B Pruned i1 can safely use up to 219K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L20 48GB See all hardware for Codestral RAG 19B Pruned i1

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