Can gemma 3 12b it run on Radeon PRO W7900 DS 48GB?

YES — Runs Great

C48Usable

Estimated from fit model

gemma 3 12b it needs ~14.4 GB VRAM. Radeon PRO W7900 DS 48GB has 48.0 GB. With Q4_K_M quantization, expect ~70 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 14.4 GB, 69.6 tok/s, Runs well

14.4 GB required48.0 GB available

30% VRAM used

Fit status

Runs well

Decode

69.6 tok/s

TTFT

2780 ms

Safe context

398K

Memory

14.4 GB / 48.0 GB

Memory breakdown

Weights7.3 GB

KV Cache1.4 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsgemma 3 12b it on Radeon PRO W7900 DS 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: 69.6 tok/s decode · 2.8s TTFT (warm) · 174 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	69.6 tok/s	1516 ms	398K
Coding	C	Runs well	69.6 tok/s	2780 ms	398K
Agentic Coding	C	Runs well	69.6 tok/s	4044 ms	398K
Reasoning	C	Runs well	69.6 tok/s	3285 ms	398K
RAG	C	Runs well	69.6 tok/s	5055 ms	398K

Quantization options

How gemma 3 12b it (12B params) fits at each quantization level on Radeon PRO W7900 DS 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

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

Mac Studio M3 Ultra 96GBOpción económica

96 GB Unified (+48)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.76.1 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Frequently asked questions

Can Radeon PRO W7900 DS 48GB run gemma 3 12b it?

Yes, Radeon PRO W7900 DS 48GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 69.6 tok/s.

How much VRAM does gemma 3 12b it need?

gemma 3 12b it (12B parameters) requires approximately 14.4 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 PRO W7900 DS 48GB?

On Radeon PRO W7900 DS 48GB, gemma 3 12b it achieves approximately 69.6 tokens per second decode speed with a time-to-first-token of 2780ms using Q4_K_M quantization.

Can Radeon PRO W7900 DS 48GB run gemma 3 12b it for coding?

For coding workloads, gemma 3 12b it on Radeon PRO W7900 DS 48GB receives a C grade with 69.6 tok/s and 398K context.

What context window can gemma 3 12b it use on Radeon PRO W7900 DS 48GB?

On Radeon PRO W7900 DS 48GB, gemma 3 12b it can safely use up to 398K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon PRO W7900 DS 48GB See all hardware for gemma 3 12b it

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