Can gemma 3 12b it run on Radeon AI PRO R9700 32GB?

YES — Runs Great

C49Usable

Estimated from fit model

gemma 3 12b it needs ~12.8 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~52 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 12.8 GB, 51.6 tok/s, Runs well

12.8 GB required32.0 GB available

40% VRAM used

Fit status

Runs well

Decode

51.6 tok/s

TTFT

3753 ms

Safe context

234K

Memory

12.8 GB / 32.0 GB

Memory breakdown

Weights7.3 GB

KV Cache1.4 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsgemma 3 12b it on Radeon AI PRO R9700 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: 51.6 tok/s decode · 3.8s TTFT (warm) · 129 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	51.6 tok/s	2047 ms	234K
Coding	C	Runs well	51.6 tok/s	3753 ms	234K
Agentic Coding	C	Runs well	51.6 tok/s	5459 ms	234K
Reasoning	C	Runs well	51.6 tok/s	4435 ms	234K
RAG	C	Runs well	51.6 tok/s	6824 ms	234K

Quantization options

How gemma 3 12b it (12B params) fits at each quantization level on Radeon AI PRO R9700 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

Upgrade-Optionen

Hardware, die gemma 3 12b it gut ausführt

NVIDIA A100 40GBGünstige Wahl

40 GB VRAM (+8)1555 GB/s (+915)

Raises estimated decode speed by about 226%.168 tok/s Dekodierung

Raises estimated decode speed by about 226%.

Adds memory headroom for longer context windows and future model growth.

ca. $10,000 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run gemma 3 12b it?

Yes, Radeon AI PRO R9700 32GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 51.6 tok/s.

How much VRAM does gemma 3 12b it need?

gemma 3 12b it (12B parameters) requires approximately 12.8 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 Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, gemma 3 12b it achieves approximately 51.6 tokens per second decode speed with a time-to-first-token of 3753ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run gemma 3 12b it for coding?

For coding workloads, gemma 3 12b it on Radeon AI PRO R9700 32GB receives a C grade with 51.6 tok/s and 234K context.

What context window can gemma 3 12b it use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, gemma 3 12b it can safely use up to 234K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for gemma 3 12b it

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