Can DevStral 7B run on NVIDIA A2 16GB?

YES — Runs Great

A76Great

Estimated from fit model

DevStral 7B needs ~9.0 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~39 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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) — 9.0 GB, 39.3 tok/s, Runs well

9.0 GB required16.0 GB available

56% VRAM used

Fit status

Runs well

Decode

39.3 tok/s

TTFT

4929 ms

Safe context

Memory

9.0 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDevStral 7B on NVIDIA A2 16GB
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: 39.3 tok/s decode · 4.9s TTFT (warm) · 98 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	A	Runs well	39.3 tok/s	2689 ms	8K
Coding	A	Runs well	39.3 tok/s	4929 ms	8K
Agentic Coding	A	Runs well	39.3 tok/s	7170 ms	8K
Reasoning	A	Runs well	39.3 tok/s	5826 ms	8K
RAG	A	Runs well	39.3 tok/s	8963 ms	8K

Inference speed

DevStral 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DevStral 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	95.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	94.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	94.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	60.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	59.8	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	55.4	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	48.7	Fits
RTX 4060 8GB	8 GB	Q4_K_M	46.0	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How DevStral 7B (7B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A72
Q3_K_S	3	3.4 GB	Low	A72
NVFP4	4	3.9 GB	Medium	A73
Q4_K_M	4	4.3 GB	Medium	A73
Q5_K_M	5	5.0 GB	High	A74
Q6_K	6	5.7 GB	High	A75
Q8_0Best for your GPU	8	7.5 GB	Very High	A76
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run DevStral 7B on your machine.

Run

ollama run devstral

Your hardware

More models your NVIDIA A2 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	30.5 tok/s
Qwen 3 14B	14B	S	19.7 tok/s
Qwen 3 8B	8B	S	34.4 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	18.7 tok/s
GPT-OSS 20B	21B	A	17.4 tok/s

Frequently asked questions

Can NVIDIA A2 16GB run DevStral 7B?

Yes, NVIDIA A2 16GB can run DevStral 7B with a A grade (Runs well). Expected decode speed: 39.3 tok/s.

How much VRAM does DevStral 7B need?

DevStral 7B (7B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.

What is the best quantization for DevStral 7B?

The recommended quantization for DevStral 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will DevStral 7B run at on NVIDIA A2 16GB?

On NVIDIA A2 16GB, DevStral 7B achieves approximately 39.3 tokens per second decode speed with a time-to-first-token of 4929ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run DevStral 7B for coding?

For coding workloads, DevStral 7B on NVIDIA A2 16GB receives a A grade with 39.3 tok/s and 8K context.

What context window can DevStral 7B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, DevStral 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB See all hardware for DevStral 7B

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