Will It Run AI

Can Devstral Small 2 24B Instruct run on GTX 1070 Ti 8GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

Devstral Small 2 24B Instruct needs ~19.1 GB but GTX 1070 Ti 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity
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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) — 19.1 GB, exceeds 8.0 GB available
19.1 GB required8.0 GB available
239% VRAM needed

11.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

4K

Memory

19.1 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights14.6 GB
KV Cache2.4 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsDevstral Small 2 24B Instruct on GTX 1070 Ti 8GB
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 · 96.8s 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 19.1 GB, but this setup only exposes 8.0 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

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy2.0 tok/s52800 ms4K
CodingFToo heavy2.0 tok/s96800 ms4K
Agentic CodingFToo heavy2.0 tok/s140800 ms4K
ReasoningFToo heavy2.0 tok/s114400 ms4K
RAGFToo heavy2.0 tok/s176000 ms4K

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowF0
Q3_K_S
3
11.8 GB
LowF0
NVFP4
4
13.4 GB
MediumF0
Q4_K_M
4
14.6 GB
MediumF0
Q5_K_M
5
17.3 GB
HighF0
Q6_K
6
19.7 GB
HighF0
Q8_0
8
25.7 GB
Very HighF0
F16
16
49.2 GB
MaximumF0

升级选项

能流畅运行 Devstral Small 2 24B Instruct 的硬件

NVIDIARTX 4000 Ada 20GB经济之选
20 GB VRAM (+12)360 GB/s (+104)
S
Makes the model fit on the accelerator instead of staying completely out of reach.15 tok/s 解码

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.

~$1,250 MSRP

NVIDIARTX 3090 24GB最佳性价比
24 GB VRAM (+16)936 GB/s (+680)
S
Makes the model fit on the accelerator instead of staying completely out of reach.48.1 tok/s 解码

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.

~$1,499 MSRP

NVIDIARTX 4090 24GBNVIDIA 升级
24 GB VRAM (+16)1008 GB/s (+752)
S
Makes the model fit on the accelerator instead of staying completely out of reach.56.3 tok/s 解码

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.

~$1,599 MSRP

Frequently asked questions

Can GTX 1070 Ti 8GB run Devstral Small 2 24B Instruct?

No, Devstral Small 2 24B Instruct requires more memory than GTX 1070 Ti 8GB provides.

How much VRAM does Devstral Small 2 24B Instruct need?

Devstral Small 2 24B Instruct (24B parameters) requires approximately 19.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral Small 2 24B Instruct?

The recommended quantization for Devstral Small 2 24B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral Small 2 24B Instruct run at on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, Devstral Small 2 24B Instruct achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can GTX 1070 Ti 8GB run Devstral Small 2 24B Instruct for coding?

For coding workloads, Devstral Small 2 24B Instruct on GTX 1070 Ti 8GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Devstral Small 2 24B Instruct use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, Devstral Small 2 24B Instruct can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Devstral Small 2 24B Instruct feels slow on GTX 1070 Ti 8GB?

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 1070 Ti 8GBSee all hardware for Devstral Small 2 24B Instruct
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