Can gemma 3 12b it run on NVIDIA V100 32GB?

YES — Runs Great

C50Usable

Estimated from fit model

gemma 3 12b it needs ~13.1 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~82 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 13.1 GB, 82.4 tok/s, Runs well

13.1 GB required32.0 GB available

41% VRAM used

Fit status

Runs well

Decode

82.4 tok/s

TTFT

2350 ms

Safe context

231K

Memory

13.1 GB / 32.0 GB

Memory breakdown

Weights7.3 GB

KV Cache1.4 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsgemma 3 12b it on NVIDIA V100 32GB
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: 82.4 tok/s decode · 2.4s TTFT (warm) · 206 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	82.4 tok/s	1282 ms	231K
Coding	C	Runs well	82.4 tok/s	2350 ms	231K
Agentic Coding	C	Runs well	82.4 tok/s	3418 ms	231K
Reasoning	C	Runs well	82.4 tok/s	2777 ms	231K
RAG	C	Runs well	82.4 tok/s	4273 ms	231K

Quantization options

How gemma 3 12b it (12B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	C44
Q3_K_S	3	5.9 GB	Low	C44
NVFP4	4	6.7 GB	Medium	C44
Q4_K_M	4	7.3 GB	Medium	C45
Q5_K_M	5	8.6 GB	High	C45
Q6_K	6	9.8 GB	High	C46
Q8_0	8	12.8 GB	Very High	C47
F16Best for your GPU	16	24.6 GB	Maximum	C49

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

Frequently asked questions

Can NVIDIA V100 32GB run gemma 3 12b it?

Yes, NVIDIA V100 32GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 82.4 tok/s.

How much VRAM does gemma 3 12b it need?

gemma 3 12b it (12B parameters) requires approximately 13.1 GB of memory with Q4_K_M quantization.

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

The recommended quantization for gemma 3 12b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 12b it run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, gemma 3 12b it achieves approximately 82.4 tokens per second decode speed with a time-to-first-token of 2350ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run gemma 3 12b it for coding?

For coding workloads, gemma 3 12b it on NVIDIA V100 32GB receives a C grade with 82.4 tok/s and 231K context.

What context window can gemma 3 12b it use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, gemma 3 12b it can safely use up to 231K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See 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-v100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

82.4 tok/s

1282 ms

231K

Coding

Runs well

82.4 tok/s

2350 ms

231K

Agentic Coding

Runs well

82.4 tok/s

3418 ms

231K

Reasoning

Runs well

82.4 tok/s

2777 ms

231K

RAG

Runs well

82.4 tok/s

4273 ms

231K

Quantization options

How gemma 3 12b it (12B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	C44
Q3_K_S	3	5.9 GB	Low	C44
NVFP4	4	6.7 GB	Medium	C44
Q4_K_M	4	7.3 GB	Medium	C45
Q5_K_M	5	8.6 GB	High	C45
Q6_K	6	9.8 GB	High	C46
Q8_0	8	12.8 GB	Very High	C47
F16Best for your GPU	16	24.6 GB	Maximum	C49

Frequently asked questions

Can NVIDIA V100 32GB run gemma 3 12b it?

Yes, NVIDIA V100 32GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 82.4 tok/s.

How much VRAM does gemma 3 12b it need?

gemma 3 12b it (12B parameters) requires approximately 13.1 GB of memory with Q4_K_M quantization.

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

The recommended quantization for gemma 3 12b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 12b it run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, gemma 3 12b it achieves approximately 82.4 tokens per second decode speed with a time-to-first-token of 2350ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run gemma 3 12b it for coding?

For coding workloads, gemma 3 12b it on NVIDIA V100 32GB receives a C grade with 82.4 tok/s and 231K context.

What context window can gemma 3 12b it use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, gemma 3 12b it can safely use up to 231K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.