Can Codestral 2 25.08 run on NVIDIA A100 40GB?

YES — Runs Great

S87Excellent

Estimated from fit model

Codestral 2 25.08 needs ~20.8 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~93 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) — 20.8 GB, 93.4 tok/s, Runs well

20.8 GB required40.0 GB available

52% VRAM used

Fit status

Runs well

Decode

93.4 tok/s

TTFT

2072 ms

Safe context

142K

Memory

20.8 GB / 40.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsCodestral 2 25.08 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: 93.4 tok/s decode · 2.1s TTFT (warm) · 234 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	S	Runs well	93.4 tok/s	1130 ms	142K
Coding	S	Runs well	93.4 tok/s	2072 ms	142K
Agentic Coding	S	Runs well	93.4 tok/s	3014 ms	142K
Reasoning	S	Runs well	93.4 tok/s	2449 ms	142K
RAG	S	Runs well	93.4 tok/s	3767 ms	142K

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	A78
Q3_K_S	3	10.8 GB	Low	A79
NVFP4	4	12.3 GB	Medium	A79
Q4_K_M	4	13.4 GB	Medium	A80
Q5_K_M	5	15.8 GB	High	A81
Q6_K	6	18.0 GB	High	A82
Q8_0Best for your GPU	8	23.5 GB	Very High	A83
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 NVIDIA A100 40GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	197.5 tok/s
Qwen 3.5 27B	27B	S	85.7 tok/s
Qwen 3.6 27B	27B	S	53.4 tok/s
Qwen 3.6 35B A3B	35B	S	166 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	204.3 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run Codestral 2 25.08?

Yes, NVIDIA A100 40GB can run Codestral 2 25.08 with a S grade (Runs well). Expected decode speed: 93.4 tok/s.

How much VRAM does Codestral 2 25.08 need?

Codestral 2 25.08 (22B parameters) requires approximately 20.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 2 25.08?

The recommended quantization for Codestral 2 25.08 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 2 25.08 run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Codestral 2 25.08 achieves approximately 93.4 tokens per second decode speed with a time-to-first-token of 2072ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run Codestral 2 25.08 for coding?

For coding workloads, Codestral 2 25.08 on NVIDIA A100 40GB receives a S grade with 93.4 tok/s and 142K context.

What context window can Codestral 2 25.08 use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Codestral 2 25.08 can safely use up to 142K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for Codestral 2 25.08

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