Can Codestral RAG 19B Pruned i1 run on NVIDIA A30 24GB?

YES — Runs Great

B55Good

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~17.4 GB VRAM. NVIDIA A30 24GB has 24.0 GB. With Q4_K_M quantization, expect ~63 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) — 17.4 GB, 62.8 tok/s, Runs well

17.4 GB required24.0 GB available

73% VRAM used

Fit status

Runs well

Decode

62.8 tok/s

TTFT

3083 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 A30 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: 62.8 tok/s decode · 3.1s TTFT (warm) · 157 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	62.8 tok/s	1682 ms	63K
Coding	B	Runs well	62.8 tok/s	3083 ms	63K
Agentic Coding	B	Runs well	62.8 tok/s	4485 ms	63K
Reasoning	B	Runs well	62.8 tok/s	3644 ms	63K
RAG	B	Runs well	62.8 tok/s	5606 ms	63K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on NVIDIA A30 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

Frequently asked questions

Can NVIDIA A30 24GB run Codestral RAG 19B Pruned i1?

Yes, NVIDIA A30 24GB can run Codestral RAG 19B Pruned i1 with a B grade (Runs well). Expected decode speed: 62.8 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 A30 24GB?

On NVIDIA A30 24GB, Codestral RAG 19B Pruned i1 achieves approximately 62.8 tokens per second decode speed with a time-to-first-token of 3083ms using Q4_K_M quantization.

Can NVIDIA A30 24GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on NVIDIA A30 24GB receives a B grade with 62.8 tok/s and 63K context.

What context window can Codestral RAG 19B Pruned i1 use on NVIDIA A30 24GB?

On NVIDIA A30 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 A30 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-a30-24gb" 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

62.8 tok/s

1682 ms

63K

Coding

Runs well

62.8 tok/s

3083 ms

63K

Agentic Coding

Runs well

62.8 tok/s

4485 ms

63K

Reasoning

Runs well

62.8 tok/s

3644 ms

63K

RAG

Runs well

62.8 tok/s

5606 ms

63K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on NVIDIA A30 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