Can Mistral Small 3.1 24B run on GTX 1660 Super 6GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Mistral Small 3.1 24B needs ~18.9 GB but GTX 1660 Super 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 18.9 GB, exceeds 6.0 GB available

18.9 GB required6.0 GB available

315% VRAM needed

12.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

95114 ms

Safe context

Memory

18.9 GB / 6.0 GB

Offload

70%

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMistral Small 3.1 24B on GTX 1660 Super 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: 2.0 tok/s decode · 95.1s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 18.9 GB, but this setup only exposes 6.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	51880 ms	4K
Coding	F	Too heavy	2.0 tok/s	95114 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	138348 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	112408 ms	4K
RAG	F	Too heavy	2.0 tok/s	172935 ms	4K

Quantization options

How Mistral Small 3.1 24B (24B params) fits at each quantization level on GTX 1660 Super 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	F0
Q3_K_S	3	11.8 GB	Low	F0
NVFP4	4	13.4 GB	Medium	F0
Q4_K_M	4	14.6 GB	Medium	F0
Q5_K_M	5	17.3 GB	High	F0
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
F16	16	49.2 GB	Maximum	F0

Opciones de mejora

Hardware que ejecuta bien Mistral Small 3.1 24B

RTX 4000 Ada 20GBOpción económica

20 GB VRAM (+14)360 GB/s (+24)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.15 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,250 MSRP

RTX 3090 24GBMejor relación calidad-precio

24 GB VRAM (+18)936 GB/s (+600)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.48.1 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,499 MSRP

RTX 4090 24GBMejora NVIDIA

24 GB VRAM (+18)1008 GB/s (+672)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.56.3 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,599 MSRP

Frequently asked questions

Can GTX 1660 Super 6GB run Mistral Small 3.1 24B?

No, Mistral Small 3.1 24B requires more memory than GTX 1660 Super 6GB provides.

How much VRAM does Mistral Small 3.1 24B need?

Mistral Small 3.1 24B (24B parameters) requires approximately 18.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 3.1 24B?

The recommended quantization for Mistral Small 3.1 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.1 24B run at on GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, Mistral Small 3.1 24B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 95114ms using Q4_K_M quantization.

Can GTX 1660 Super 6GB run Mistral Small 3.1 24B for coding?

For coding workloads, Mistral Small 3.1 24B on GTX 1660 Super 6GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Mistral Small 3.1 24B use on GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, Mistral Small 3.1 24B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Small 3.1 24B feels slow on GTX 1660 Super 6GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for GTX 1660 Super 6GB See all hardware for Mistral Small 3.1 24B

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