Can gemma 3 12b it run on Quadro RTX 8000 48GB?

YES — Runs Great

C47Usable

○Estimated from fit model

gemma 3 12b it needs ~14.7 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~63 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 14.7 GB, 63.3 tok/s, Runs well

14.7 GB required48.0 GB available

31% VRAM used

Fit status

Runs well

Decode

63.3 tok/s

TTFT

3056 ms

Safe context

395K

Memory

14.7 GB / 48.0 GB

Memory breakdown

Weights7.3 GB

KV Cache1.4 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsgemma 3 12b it on Quadro RTX 8000 48GB
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: 63.3 tok/s decode · 3.1s TTFT (warm) · 158 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	63.3 tok/s	1667 ms	395K
Coding	C	Runs well	63.3 tok/s	3056 ms	395K
Agentic Coding	C	Runs well	63.3 tok/s	4446 ms	395K
Reasoning	C	Runs well	63.3 tok/s	3612 ms	395K
RAG	C	Runs well	63.3 tok/s	5557 ms	395K

Quantization options

How gemma 3 12b it (12B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	C42
Q3_K_S	3	5.9 GB	Low	C42
NVFP4	4	6.7 GB	Medium	C42
Q4_K_M	4	7.3 GB	Medium	C42
Q5_K_M	5	8.6 GB	High	C43
Q6_K	6	9.8 GB	High	C43
Q8_0	8	12.8 GB	Very High	C44
F16Best for your GPU	16	24.6 GB	Maximum	C48