Can Codestral RAG 19B Pruned i1 run on RTX A6000 48GB?

YES — Runs Great

C48Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~19.8 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~50 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) — 19.8 GB, 50.4 tok/s, Runs well

19.8 GB required48.0 GB available

41% VRAM used

Fit status

Runs well

Decode

50.4 tok/s

TTFT

3844 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 RTX A6000 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: 50.4 tok/s decode · 3.8s TTFT (warm) · 126 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	50.4 tok/s	2097 ms	219K
Coding	C	Runs well	50.4 tok/s	3844 ms	219K
Agentic Coding	C	Runs well	50.4 tok/s	5592 ms	219K
Reasoning	C	Runs well	50.4 tok/s	4543 ms	219K
RAG	C	Runs well	50.4 tok/s	6990 ms	219K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on RTX A6000 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

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

AMD Instinct MI210 64GBコスパ重視

64 GB VRAM (+16)1638 GB/s (+870)

Raises estimated decode speed by about 91%.96.1 tok/sデコード

Raises estimated decode speed by about 91%.

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

〜$10,000 MSRP

Frequently asked questions

Can RTX A6000 48GB run Codestral RAG 19B Pruned i1?

Yes, RTX A6000 48GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 50.4 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 RTX A6000 48GB?

On RTX A6000 48GB, Codestral RAG 19B Pruned i1 achieves approximately 50.4 tokens per second decode speed with a time-to-first-token of 3844ms using Q4_K_M quantization.

Can RTX A6000 48GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on RTX A6000 48GB receives a C grade with 50.4 tok/s and 219K context.

What context window can Codestral RAG 19B Pruned i1 use on RTX A6000 48GB?

On RTX A6000 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 RTX A6000 48GB See all hardware for Codestral RAG 19B Pruned i1

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