Can Codestral RAG 19B Pruned i1 run on RTX 5000 Ada 32GB?

YES — Runs Great

C51Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~18.2 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 18.2 GB, 39.8 tok/s, Runs well

18.2 GB required32.0 GB available

57% VRAM used

Fit status

Runs well

Decode

39.8 tok/s

TTFT

4869 ms

Safe context

115K

Memory

18.2 GB / 32.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on RTX 5000 Ada 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: 39.8 tok/s decode · 4.9s TTFT (warm) · 99 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	39.8 tok/s	2656 ms	115K
Coding	C	Runs well	39.8 tok/s	4869 ms	115K
Agentic Coding	C	Runs well	39.8 tok/s	7083 ms	115K
Reasoning	C	Runs well	39.8 tok/s	5755 ms	115K
RAG	C	Runs well	39.8 tok/s	8853 ms	115K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on RTX 5000 Ada 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

アップグレードオプション

Codestral RAG 19B Pruned i1を快適に動かすハードウェア

NVIDIA A100 40GBコスパ重視

40 GB VRAM (+8)1555 GB/s (+979)

Raises estimated decode speed by about 183%.112.7 tok/sデコード

Raises estimated decode speed by about 183%.

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

〜$10,000 MSRP

Frequently asked questions

Can RTX 5000 Ada 32GB run Codestral RAG 19B Pruned i1?

Yes, RTX 5000 Ada 32GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 39.8 tok/s.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 18.2 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 RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Codestral RAG 19B Pruned i1 achieves approximately 39.8 tokens per second decode speed with a time-to-first-token of 4869ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on RTX 5000 Ada 32GB receives a C grade with 39.8 tok/s and 115K context.

What context window can Codestral RAG 19B Pruned i1 use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Codestral RAG 19B Pruned i1 can safely use up to 115K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for Codestral RAG 19B Pruned i1

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