Can gemma 3 12b it run on RX 5600 XT 6GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

gemma 3 12b it needs ~10.2 GB but RX 5600 XT 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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) — 10.2 GB, exceeds 6.0 GB available

10.2 GB required6.0 GB available

170% VRAM needed

4.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.0 tok/s

TTFT

38762 ms

Safe context

Memory

10.2 GB / 6.0 GB

Offload

40%

Memory breakdown

Weights7.3 GB

KV Cache1.4 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsgemma 3 12b it on RX 5600 XT 6GB
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: 5.0 tok/s decode · 38.8s TTFT (warm) · 13 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 10.2 GB, but this setup only exposes 6.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	5.8 tok/s	18199 ms	4K
Coding	F	Too heavy	5.0 tok/s	38762 ms	4K
Agentic Coding	F	Too heavy	3.8 tok/s	73947 ms	4K
Reasoning	F	Too heavy	5.0 tok/s	45809 ms	4K
RAG	F	Too heavy	3.8 tok/s	92434 ms	4K

Quantization options

How gemma 3 12b it (12B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	F0
Q3_K_S	3	5.9 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs gemma 3 12b it well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.22.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+10)320 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.27.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$349 MSRP

RX 7700 XT 12GBAMD upgrade

12 GB VRAM (+6)432 GB/s (+144)

Makes the model fit on the accelerator instead of staying completely out of reach.35.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

Frequently asked questions

Can RX 5600 XT 6GB run gemma 3 12b it?

No, gemma 3 12b it requires more memory than RX 5600 XT 6GB provides.

How much VRAM does gemma 3 12b it need?

gemma 3 12b it (12B parameters) requires approximately 10.2 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 RX 5600 XT 6GB?

On RX 5600 XT 6GB, gemma 3 12b it achieves approximately 5.0 tokens per second decode speed with a time-to-first-token of 38762ms using Q4_K_M quantization.

Can RX 5600 XT 6GB run gemma 3 12b it for coding?

For coding workloads, gemma 3 12b it on RX 5600 XT 6GB receives a F grade with 5.0 tok/s and 4K context.

What context window can gemma 3 12b it use on RX 5600 XT 6GB?

On RX 5600 XT 6GB, gemma 3 12b it can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if gemma 3 12b it feels slow on RX 5600 XT 6GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RX 5600 XT 6GB See all hardware for gemma 3 12b it

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