Can Codestral RAG 19B Pruned i1 run on NVIDIA A100 40GB?

YES — Runs Great

C52Usable

○Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~19.0 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~113 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) — 19.0 GB, 112.7 tok/s, Runs well

19.0 GB required40.0 GB available

48% VRAM used

Fit status

Runs well

Decode

112.7 tok/s

TTFT

1718 ms

Safe context

167K

Memory

19.0 GB / 40.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on NVIDIA A100 40GB
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: 112.7 tok/s decode · 1.7s TTFT (warm) · 282 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	112.7 tok/s	937 ms	167K
Coding	C	Runs well	112.7 tok/s	1718 ms	167K
Agentic Coding	C	Runs well	112.7 tok/s	2499 ms	167K
Reasoning	C	Runs well	112.7 tok/s	2030 ms	167K
RAG	C	Runs well	112.7 tok/s	3123 ms	167K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C43
Q3_K_S	3	9.3 GB	Low	C43
NVFP4	4	10.6 GB	Medium	C44
Q4_K_M	4	11.6 GB	Medium	C44
Q5_K_M	5	13.7 GB	High	C45
Q6_K	6	15.6 GB	High	C46
Q8_0Best for your GPU	8	20.3 GB	Very High	C48
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

See all results for NVIDIA A100 40GB See all hardware for Codestral RAG 19B Pruned i1

URL: https://willitrunai.com/can-run/hf-mradermacher--codestral-rag-19b-pruned-i1-gguf-on-a100-40gb

⇱ Codestral RAG 19B Pruned i1 on NVIDIA A100 40GB? YES