Will It Run AI

Can gemma 3 12b it run on GTX 1070 Ti 8GB?

YES — With Q3_K_S

D38Poor
Estimated from fit model

gemma 3 12b it needs ~9.3 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q3_K_S quantization, expect ~13 tok/s.

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

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.

gemma 3 12b it at Q4_K_M needs 10.7 GB — too much for GTX 1070 Ti 8GB (8.0 GB). Runs at Q3_K_S (9.3 GB) with low quality. 2 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 10.7 GB, exceeds 8.0 GB available
10.7 GB required8.0 GB available
134% VRAM needed

2.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.9 tok/s

TTFT

24492 ms

Safe context

4K

Memory

10.7 GB / 8.0 GB

Offload

30%

Memory breakdown

Weights7.3 GB
KV Cache1.4 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsgemma 3 12b it on GTX 1070 Ti 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 7.9 tok/s decode · 24.5s TTFT (warm) · 20 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 10% 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 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy9.2 tok/s11511 ms4K
CodingFToo heavy7.9 tok/s24492 ms4K
Agentic CodingFToo heavy6.0 tok/s46693 ms4K
ReasoningFToo heavy7.9 tok/s28945 ms4K
RAGFToo heavy6.0 tok/s58366 ms4K

Quantization options

How gemma 3 12b it (12B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
4.7 GB
LowC53
Q3_K_S
3
5.9 GB
LowF0
NVFP4
4
6.7 GB
MediumF0
Q4_K_M
4
7.3 GB
MediumF0
Q5_K_M
5
8.6 GB
HighF0
Q6_K
6
9.8 GB
HighF0
Q8_0
8
12.8 GB
Very HighF0
F16
16
24.6 GB
MaximumF0

Get started

Copy-paste commands to run gemma 3 12b it on your machine.

Run

lms load hf-maziyarpanahi--gemma-3-12b-it-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien gemma 3 12b it

NVIDIARTX 3060 12GBOpción económica
12 GB VRAM (+4)360 GB/s (+104)
C
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.32.5 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.

~$329 MSRP

NVIDIARTX 5060 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+8)448 GB/s (+192)
C
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.37.9 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.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBMejora NVIDIA
16 GB VRAM (+8)288 GB/s (+32)
C
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.28.7 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.

~$499 MSRP

Frequently asked questions

Can GTX 1070 Ti 8GB run gemma 3 12b it?

Yes, GTX 1070 Ti 8GB can run gemma 3 12b it at Q3_K_S quantization (Very compromised (needs ~0.8 GB host RAM)). The recommended Q4_K_M requires 10.7 GB which exceeds available memory, but at Q3_K_S it needs only 9.3 GB. Expected decode speed: 12.6 tok/s.

How much VRAM does gemma 3 12b it need?

gemma 3 12b it (12B parameters) requires approximately 10.7 GB at Q4_K_M quantization. On GTX 1070 Ti 8GB, it fits at Q3_K_S using 9.3 GB.

What is the best quantization for gemma 3 12b it?

The recommended quantization is Q4_K_M, but on GTX 1070 Ti 8GB the best fitting quantization is Q3_K_S, which uses 9.3 GB.

What speed will gemma 3 12b it run at on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, gemma 3 12b it achieves approximately 12.6 tokens per second decode speed with a time-to-first-token of 15414ms using Q3_K_S quantization.

Can GTX 1070 Ti 8GB run gemma 3 12b it for coding?

For coding workloads, gemma 3 12b it on GTX 1070 Ti 8GB receives a F grade with 7.9 tok/s and 4K context.

What context window can gemma 3 12b it use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, gemma 3 12b it can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if gemma 3 12b it feels slow on GTX 1070 Ti 8GB?

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.

See all results for GTX 1070 Ti 8GBSee all hardware for gemma 3 12b it
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--gemma-3-12b-it-gguf-on-gtx-1070-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: