Can Codestral 22B v0.1 run on RTX PRO 6000 Blackwell Workstation Edition 96GB?

YES — Runs Great

C48Usable

○Estimated from fit model

Codestral 22B v0.1 needs ~26.8 GB VRAM. RTX PRO 6000 Blackwell Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~112 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) — 26.8 GB, 112.2 tok/s, Runs well

26.8 GB required96.0 GB available

28% VRAM used

Fit status

Runs well

Decode

112.2 tok/s

TTFT

1726 ms

Safe context

445K

Memory

26.8 GB / 96.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 on RTX PRO 6000 Blackwell Workstation Edition 96GB
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: 112.2 tok/s decode · 1.7s TTFT (warm) · 280 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	112.2 tok/s	941 ms	445K
Coding	C	Runs well	112.2 tok/s	1726 ms	445K
Agentic Coding	C	Runs well	112.2 tok/s	2511 ms	445K
Reasoning	C	Runs well	112.2 tok/s	2040 ms	445K
RAG	C	Runs well	112.2 tok/s	3138 ms	445K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on RTX PRO 6000 Blackwell Workstation Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	D39
Q3_K_S	3	10.8 GB	Low	D39
NVFP4	4	12.3 GB	Medium	D40
Q4_K_M	4	13.4 GB	Medium	D40
Q5_K_M	5	15.8 GB	High	D40
Q6_K	6	18.0 GB	High	C40
Q8_0	8	23.5 GB	Very High	C41
F16Best for your GPU	16	45.1 GB	Maximum	C45

Get started

Copy-paste commands to run Codestral 22B v0.1 on your machine.

Run

lms load hf-bartowski--codestral-22b-v0-1-gguf && lms server start

Frequently asked questions

See all results for RTX PRO 6000 Blackwell Workstation Edition 96GB See all hardware for Codestral 22B v0.1

URL: https://willitrunai.com/can-run/hf-bartowski--codestral-22b-v0-1-gguf-on-rtx-pro-6000-blackwell-96gb

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