Can Codestral 2 25.08 run on RTX PRO 5000 Blackwell 48GB?

YES — Runs Great

S85Excellent

○Estimated from fit model

Codestral 2 25.08 needs ~21.6 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~81 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 21.6 GB, 80.8 tok/s, Runs well

21.6 GB required48.0 GB available

45% VRAM used

Fit status

Runs well

Decode

80.8 tok/s

TTFT

2397 ms

Safe context

189K

Memory

21.6 GB / 48.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCodestral 2 25.08 on RTX PRO 5000 Blackwell 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: 80.8 tok/s decode · 2.4s TTFT (warm) · 202 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	A	Runs well	80.8 tok/s	1308 ms	189K
Coding	S	Runs well	80.8 tok/s	2397 ms	189K
Agentic Coding	S	Runs well	80.8 tok/s	3487 ms	189K
Reasoning	S	Runs well	80.8 tok/s	2833 ms	189K
RAG	S	Runs well	80.8 tok/s	4359 ms	189K

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	A77
Q3_K_S	3	10.8 GB	Low	A77
NVFP4	4	12.3 GB	Medium	A78
Q4_K_M	4	13.4 GB	Medium	A78
Q5_K_M	5	15.8 GB	High	A79
Q6_K	6	18.0 GB	High	A80
Q8_0Best for your GPU	8	23.5 GB	Very High	A82
F16	16	45.1 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral 2 25.08 on your machine.

Run

lms load codestral-2508 && lms server start

Your hardware

More models your RTX PRO 5000 Blackwell 48GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	170.7 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	74 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	46.1 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	143.5 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	176.6 tok/s

Frequently asked questions

See all results for RTX PRO 5000 Blackwell 48GB See all hardware for Codestral 2 25.08

URL: https://willitrunai.com/can-run/codestral-2-25.08-on-rtx-pro-5000-blackwell-48gb

⇱ Codestral 2 25.08 on RTX PRO 5000 Blackwell 48GB? YES