Can gemma 3 12b it run on Radeon RX 7800M 12GB?

YES — Tight Fit

C51Usable

Estimated from fit model

gemma 3 12b it needs ~10.8 GB VRAM. Radeon RX 7800M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 10.8 GB, 34.8 tok/s, Tight fit

10.8 GB required12.0 GB available

90% VRAM used

Fit status

Tight fit

Decode

34.8 tok/s

TTFT

5560 ms

Safe context

29K

Memory

10.8 GB / 12.0 GB

Memory breakdown

Weights7.3 GB

KV Cache1.4 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsgemma 3 12b it on Radeon RX 7800M 12GB
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: 34.8 tok/s decode · 5.6s TTFT (warm) · 87 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	Tight fit	34.8 tok/s	3033 ms	29K
Coding	C	Tight fit	34.8 tok/s	5560 ms	29K
Agentic Coding	C	Runs with offload (needs ~0.1 GB host RAM)	25.1 tok/s	11228 ms	29K
Reasoning	C	Tight fit	34.8 tok/s	6571 ms	29K
RAG	C	Runs with offload (needs ~0.1 GB host RAM)	25.1 tok/s	14035 ms

Quantization options

How gemma 3 12b it (12B params) fits at each quantization level on Radeon RX 7800M 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	C52
Q3_K_S	3	5.9 GB	Low	C52
NVFP4	4

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 options

Hardware that runs gemma 3 12b it well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.22.8 tok/s decode

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

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.27.5 tok/s decode

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

~$349 MSRP

RX 9070 16GBAMD upgrade

16 GB VRAM (+4)640 GB/s (+208)

Raises estimated decode speed by about 56%.54.2 tok/s decode

Raises estimated decode speed by about 56%.

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

~$479 MSRP

Frequently asked questions

Can Radeon RX 7800M 12GB run gemma 3 12b it?

Yes, Radeon RX 7800M 12GB can run gemma 3 12b it with a C grade (Tight fit). Expected decode speed: 34.8 tok/s.

How much VRAM does gemma 3 12b it need?

gemma 3 12b it (12B parameters) requires approximately 10.8 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 RX 7800M 12GB?

On Radeon RX 7800M 12GB, gemma 3 12b it achieves approximately 34.8 tokens per second decode speed with a time-to-first-token of 5560ms using Q4_K_M quantization.

Can Radeon RX 7800M 12GB run gemma 3 12b it for coding?

For coding workloads, gemma 3 12b it on Radeon RX 7800M 12GB receives a C grade with 34.8 tok/s and 29K context.

What context window can gemma 3 12b it use on Radeon RX 7800M 12GB?

On Radeon RX 7800M 12GB, gemma 3 12b it can safely use up to 29K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon RX 7800M 12GB See all hardware for gemma 3 12b it

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