Can Codestral RAG 19B Pruned i1 run on NVIDIA L4 24GB?

YES — Runs Great

C51Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~17.4 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~17 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 17.4 GB, 16.8 tok/s, Runs well

17.4 GB required24.0 GB available

73% VRAM used

Fit status

Runs well

Decode

16.8 tok/s

TTFT

11507 ms

Safe context

63K

Memory

17.4 GB / 24.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on NVIDIA L4 24GB
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: 16.8 tok/s decode · 11.5s TTFT (warm) · 42 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	16.8 tok/s	6276 ms	63K
Coding	C	Runs well	16.8 tok/s	11507 ms	63K
Agentic Coding	C	Runs well	16.8 tok/s	16737 ms	63K
Reasoning	C	Runs well	16.8 tok/s	13599 ms	63K
RAG	C	Runs well	16.8 tok/s	20921 ms	63K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C47
Q3_K_S	3	9.3 GB	Low	C48
NVFP4	4	10.6 GB	Medium	C49
Q4_K_M	4	11.6 GB	Medium	C49
Q5_K_M	5	13.7 GB	High	C50
Q6_KBest for your GPU	6	15.6 GB	High	C49
Q8_0	8	20.3 GB	Very High	F0
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

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1492)

Raises estimated decode speed by about 517%.103.6 tok/s decode

Raises estimated decode speed by about 517%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+596)

Raises estimated decode speed by about 286%.64.9 tok/s decode

Raises estimated decode speed by about 286%.

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

~$2,499 MSRP

NVIDIA V100 32GBNVIDIA upgrade

32 GB VRAM (+8)900 GB/s (+600)

Raises estimated decode speed by about 210%.52 tok/s decode

Raises estimated decode speed by about 210%.

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

~$8,999 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Codestral RAG 19B Pruned i1?

Yes, NVIDIA L4 24GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 16.8 tok/s.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 17.4 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 L4 24GB?

On NVIDIA L4 24GB, Codestral RAG 19B Pruned i1 achieves approximately 16.8 tokens per second decode speed with a time-to-first-token of 11507ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on NVIDIA L4 24GB receives a C grade with 16.8 tok/s and 63K context.

What context window can Codestral RAG 19B Pruned i1 use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Codestral RAG 19B Pruned i1 can safely use up to 63K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB 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-l4-24gb" 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

16.8 tok/s

6276 ms

63K

Coding

Runs well

16.8 tok/s

11507 ms

63K

Agentic Coding

Runs well

16.8 tok/s

16737 ms

63K

Reasoning

Runs well

16.8 tok/s

13599 ms

63K

RAG

Runs well

16.8 tok/s

20921 ms

63K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C47
Q3_K_S	3	9.3 GB	Low	C48
NVFP4	4	10.6 GB	Medium	C49
Q4_K_M	4	11.6 GB	Medium	C49
Q5_K_M	5	13.7 GB	High	C50
Q6_KBest for your GPU	6	15.6 GB	High	C49
Q8_0	8	20.3 GB	Very High	F0
F16	16	38.9 GB	Maximum	F0