Can DevStral 7B run on RTX 2080 Ti 11GB?

YES — Runs Great

A82Great

○Estimated from fit model

DevStral 7B needs ~8.5 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~98 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) — 8.5 GB, 98.0 tok/s, Runs well

8.5 GB required11.0 GB available

77% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

Memory

8.5 GB / 11.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsDevStral 7B on RTX 2080 Ti 11GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	98.0 tok/s	1078 ms	8K
Coding	A	Runs well	98.0 tok/s	1976 ms	8K
Agentic Coding	A	Runs with offload	98.0 tok/s	2873 ms	8K
Reasoning	A	Runs well	98.0 tok/s	2335 ms	8K
RAG	A	Runs with offload	98.0 tok/s	3592 ms	8K

Quantization options

How DevStral 7B (7B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A75
Q3_K_S	3	3.4 GB	Low	A76
NVFP4	4	3.9 GB	Medium	A77
Q4_K_M	4	4.3 GB	Medium	A77
Q5_K_M	5	5.0 GB	High	A78
Q6_K	6	5.7 GB	High	A78
Q8_0Best for your GPU	8	7.5 GB	Very High	A77
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 RTX 2080 Ti 11GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	78.4 tok/s
👁 Alibaba Qwen 3 8B	8B	S	88.2 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	S	88.2 tok/s
👁 InternLM InternVL2 8B	8B	S	88.2 tok/s
👁 Mistral Ministral 3 8B	8B	A	88.2 tok/s

Frequently asked questions

See all results for RTX 2080 Ti 11GB See all hardware for DevStral 7B

URL: https://willitrunai.com/can-run/devstral-7b-on-rtx-2080-ti-11gb