Can Codestral 22B v0.1 IMat run on NVIDIA B200 180GB?

YES — Runs Great

C46Usable

Estimated from fit model

Codestral 22B v0.1 IMat needs ~35.2 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~308 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) — 35.2 GB, 308.0 tok/s, Runs well

35.2 GB required180.0 GB available

20% VRAM used

Fit status

Runs well

Decode

308.0 tok/s

TTFT

629 ms

Safe context

915K

Memory

35.2 GB / 180.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 IMat on NVIDIA B200 180GB
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: 308.0 tok/s decode · 629ms TTFT (warm) · 770 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	308.0 tok/s	350 ms	915K
Coding	C	Runs well	308.0 tok/s	629 ms	915K
Agentic Coding	C	Runs well	308.0 tok/s	914 ms	915K
Reasoning	C	Runs well	308.0 tok/s	743 ms	915K
RAG	C	Runs well	308.0 tok/s	1143 ms	915K

Quantization options

How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	D37
Q3_K_S	3	10.8 GB	Low	D37
NVFP4	4	12.3 GB	Medium	D37
Q4_K_M	4	13.4 GB	Medium	D37
Q5_K_M	5	15.8 GB	High	D37
Q6_K	6	18.0 GB	High	D37
Q8_0	8	23.5 GB	Very High	D38
F16Best for your GPU	16	45.1 GB	Maximum	C40

Get started

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

Run

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

Frequently asked questions

Can NVIDIA B200 180GB run Codestral 22B v0.1 IMat?

Yes, NVIDIA B200 180GB can run Codestral 22B v0.1 IMat with a C grade (Runs well). Expected decode speed: 308.0 tok/s.

How much VRAM does Codestral 22B v0.1 IMat need?

Codestral 22B v0.1 IMat (22B parameters) requires approximately 35.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1 IMat?

The recommended quantization for Codestral 22B v0.1 IMat is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 IMat run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Codestral 22B v0.1 IMat achieves approximately 308.0 tokens per second decode speed with a time-to-first-token of 629ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on NVIDIA B200 180GB receives a C grade with 308.0 tok/s and 915K context.

What context window can Codestral 22B v0.1 IMat use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Codestral 22B v0.1 IMat can safely use up to 915K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GB See all hardware for Codestral 22B v0.1 IMat

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