Can Devstral Small 1.1 run on NVIDIA A16 64GB?

YES — Runs Great

S87Excellent

Estimated from fit model

Devstral Small 1.1 needs ~24.7 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 24.7 GB, 34.4 tok/s, Runs well

24.7 GB required64.0 GB available

39% VRAM used

Fit status

Runs well

Decode

34.4 tok/s

TTFT

5634 ms

Safe context

131K

Memory

24.7 GB / 64.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsDevstral Small 1.1 on NVIDIA A16 64GB
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: 34.4 tok/s decode · 5.6s TTFT (warm) · 86 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	34.4 tok/s	3073 ms	131K
Coding	S	Runs well	34.4 tok/s	5634 ms	131K
Agentic Coding	S	Runs well	34.4 tok/s	8194 ms	131K
Reasoning	S	Runs well	34.4 tok/s	6658 ms	131K
RAG	S	Runs well	34.4 tok/s	10243 ms	131K

Quantization options

How Devstral Small 1.1 (24B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A81
Q3_K_S	3	11.8 GB	Low	A81
NVFP4	4	13.4 GB	Medium	A81
Q4_K_M	4	14.6 GB	Medium	A82
Q5_K_M	5	17.3 GB	High	A82
Q6_K	6	19.7 GB	High	A83
Q8_0	8	25.7 GB	Very High	A84
F16Best for your GPU	16	49.2 GB	Maximum	S87

Get started

Copy-paste commands to run Devstral Small 1.1 on your machine.

Run

lms load Devstral-Small-2507 && lms server start

Your hardware

More models your NVIDIA A16 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.8 tok/s
Qwen 3.5 27B	27B	S	30.7 tok/s
Qwen 3.6 27B	27B	S	30.8 tok/s
Qwen 3.6 35B A3B	35B	S	59.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	73.2 tok/s

Frequently asked questions

Can NVIDIA A16 64GB run Devstral Small 1.1?

Yes, NVIDIA A16 64GB can run Devstral Small 1.1 with a S grade (Runs well). Expected decode speed: 34.4 tok/s.

How much VRAM does Devstral Small 1.1 need?

Devstral Small 1.1 (24B parameters) requires approximately 24.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral Small 1.1?

The recommended quantization for Devstral Small 1.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral Small 1.1 run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Devstral Small 1.1 achieves approximately 34.4 tokens per second decode speed with a time-to-first-token of 5634ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Devstral Small 1.1 for coding?

For coding workloads, Devstral Small 1.1 on NVIDIA A16 64GB receives a S grade with 34.4 tok/s and 131K context.

What context window can Devstral Small 1.1 use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Devstral Small 1.1 can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for Devstral Small 1.1

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