Can Codestral RAG 19B Pruned i1 run on RTX A4500 20GB?

YES — Tight Fit

C51Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~17.0 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 17.0 GB, 43.1 tok/s, Tight fit

17.0 GB required20.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

43.1 tok/s

TTFT

4495 ms

Safe context

37K

Memory

17.0 GB / 20.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on RTX A4500 20GB
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: 43.1 tok/s decode · 4.5s TTFT (warm) · 108 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	43.1 tok/s	2452 ms	37K
Coding	C	Tight fit	43.1 tok/s	4495 ms	37K
Agentic Coding	C	Runs with offload	43.1 tok/s	6538 ms	37K
Reasoning	C	Tight fit	43.1 tok/s	5312 ms	37K
RAG	C	Runs with offload	43.1 tok/s	8172 ms	37K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C48
Q3_K_S	3	9.3 GB	Low	C50
NVFP4	4

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 3090 24GBBudget pick

24 GB VRAM (+4)936 GB/s (+296)

Raises estimated decode speed by about 31%.56.5 tok/s decode

Raises estimated decode speed by about 31%.

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+4)1008 GB/s (+368)

Raises estimated decode speed by about 53%.66.1 tok/s decode

Raises estimated decode speed by about 53%.

~$1,599 MSRP

RTX A5500 24GBNVIDIA upgrade

24 GB VRAM (+4)768 GB/s (+128)

This setup is broadly balanced for this model.51.7 tok/s decode

~$3,200 MSRP

Frequently asked questions

Can RTX A4500 20GB run Codestral RAG 19B Pruned i1?

Yes, RTX A4500 20GB can run Codestral RAG 19B Pruned i1 with a C grade (Tight fit). Expected decode speed: 43.1 tok/s.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 17.0 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 A4500 20GB?

On RTX A4500 20GB, Codestral RAG 19B Pruned i1 achieves approximately 43.1 tokens per second decode speed with a time-to-first-token of 4495ms using Q4_K_M quantization.

Can RTX A4500 20GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on RTX A4500 20GB receives a C grade with 43.1 tok/s and 37K context.

What context window can Codestral RAG 19B Pruned i1 use on RTX A4500 20GB?

On RTX A4500 20GB, Codestral RAG 19B Pruned i1 can safely use up to 37K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX A4500 20GB See all hardware for Codestral RAG 19B Pruned i1

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