Can DevStral 7B run on GTX 1660 Ti 6GB?

Q: What should I upgrade first if DevStral 7B feels slow on GTX 1660 Ti 6GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

B59Good

Estimated from fit model

DevStral 7B needs ~7.2 GB VRAM. GTX 1660 Ti 6GB has 6.0 GB. With Q3_K_S quantization, expect ~23 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload

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.

DevStral 7B at Q4_K_M needs 8.0 GB — too much for GTX 1660 Ti 6GB (6.0 GB). Runs at Q3_K_S (7.2 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.0 GB, exceeds 6.0 GB available

8.0 GB required6.0 GB available

133% VRAM needed

2.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

15.4 tok/s

TTFT

12598 ms

Safe context

Memory

8.0 GB / 6.0 GB

Offload

30%

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDevStral 7B on GTX 1660 Ti 6GB
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: 15.4 tok/s decode · 12.6s TTFT (warm) · 38 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Very compromised (needs ~0.6 GB host RAM)	20.4 tok/s	5168 ms	4K
Coding	F	Too heavy	14.3 tok/s	13543 ms	4K
Agentic Coding	F	Too heavy	9.5 tok/s	29569 ms	4K
Reasoning	F	Too heavy	15.4 tok/s	14889 ms	4K
RAG	F	Too heavy	9.5 tok/s	36961 ms	4K

Quantization options

How DevStral 7B (7B params) fits at each quantization level on GTX 1660 Ti 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A79
Q3_K_SBest for your GPU	3	3.4 GB	Low	A79

Get started

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

Run

ollama run devstral

Upgrade options

Hardware that runs DevStral 7B well

RTX 3050 8GBBudget pick

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.26.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$249 MSRP

RTX 5060 8GBBest value

8 GB VRAM (+2)448 GB/s (+160)

Makes the model fit on the accelerator instead of staying completely out of reach.49.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$299 MSRP

RTX 4060 8GBNVIDIA upgrade

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.35.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$299 MSRP

Frequently asked questions

Can GTX 1660 Ti 6GB run DevStral 7B?

Yes, GTX 1660 Ti 6GB can run DevStral 7B at Q3_K_S quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 8.0 GB which exceeds available memory, but at Q3_K_S it needs only 7.2 GB. Expected decode speed: 22.7 tok/s.

How much VRAM does DevStral 7B need?

DevStral 7B (7B parameters) requires approximately 8.0 GB at Q4_K_M quantization. On GTX 1660 Ti 6GB, it fits at Q3_K_S using 7.2 GB.

What is the best quantization for DevStral 7B?

The recommended quantization is Q4_K_M, but on GTX 1660 Ti 6GB the best fitting quantization is Q3_K_S, which uses 7.2 GB.

What speed will DevStral 7B run at on GTX 1660 Ti 6GB?

On GTX 1660 Ti 6GB, DevStral 7B achieves approximately 22.7 tokens per second decode speed with a time-to-first-token of 8535ms using Q3_K_S quantization.

Can GTX 1660 Ti 6GB run DevStral 7B for coding?

For coding workloads, DevStral 7B on GTX 1660 Ti 6GB receives a F grade with 14.3 tok/s and 4K context.

What context window can DevStral 7B use on GTX 1660 Ti 6GB?

On GTX 1660 Ti 6GB, DevStral 7B can safely use up to 6K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for GTX 1660 Ti 6GB See all hardware for DevStral 7B

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