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URL: https://willitrunai.com/can-run/hf-mradermacher--codestral-21b-pruned-i1-gguf-on-b100-192gb


Can Codestral 21B Pruned i1 run on B100 192GB?

YES — Runs Great

C45Usable
Estimated from fit model

Codestral 21B Pruned i1 needs ~35.7 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~294 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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) — 35.7 GB, 294.0 tok/s, Runs well
35.7 GB required192.0 GB available
19% VRAM used

Fit status

Runs well

Decode

294.0 tok/s

TTFT

659 ms

Safe context

1.0M

Memory

35.7 GB / 192.0 GB

Memory breakdown

Weights12.8 GB
KV Cache2.5 GB
Runtime1.2 GB
Headroom19.2 GB

See how fast it feels

See how fast it feelsCodestral 21B Pruned i1 on B100 192GB
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: 294.0 tok/s decode · 659ms TTFT (warm) · 735 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well294.0 tok/s359 ms1.0M
CodingCRuns well294.0 tok/s659 ms1.0M
Agentic CodingCRuns well294.0 tok/s958 ms1.0M
ReasoningCRuns well294.0 tok/s778 ms1.0M
RAGCRuns well294.0 tok/s1197 ms1.0M

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on B100 192GB (192.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
8.2 GB
LowD36
Q3_K_S
3
10.3 GB
LowD37
NVFP4
4

Get started

Copy-paste commands to run Codestral 21B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-21b-pruned-i1-gguf && lms server start

Frequently asked questions

See all results for B100 192GBSee all hardware for Codestral 21B Pruned i1
11.8 GB
Medium
D37
Q4_K_M
4
12.8 GB
MediumD37
Q5_K_M
5
15.1 GB
HighD37
Q6_K
6
17.2 GB
HighD37
Q8_0
8
22.5 GB
Very HighD37
F16Best for your GPU
16
43.1 GB
MaximumD40