Can Gemma 2 9B run on RX 7900 XT 20GB?

YES — Runs Great

A71Great

Estimated from fit model

Gemma 2 9B needs ~13.8 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~92 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 13.8 GB, 91.8 tok/s, Runs well

13.8 GB required20.0 GB available

69% VRAM used

Fit status

Runs well

Decode

91.8 tok/s

TTFT

2109 ms

Safe context

Memory

13.8 GB / 20.0 GB

Memory breakdown

Weights5.5 GB

KV Cache5.1 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsGemma 2 9B on RX 7900 XT 20GB
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: 91.8 tok/s decode · 2.1s TTFT (warm) · 230 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	B	Runs well	91.8 tok/s	1150 ms	8K
Coding	A	Runs well	91.8 tok/s	2109 ms	8K
Agentic Coding	B	Tight fit	91.8 tok/s	3067 ms	8K
Reasoning	A	Runs well	91.8 tok/s	2492 ms	8K
RAG	B	Tight fit	91.8 tok/s	3834 ms	8K

Inference speed

Gemma 2 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 2 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	125.3	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	86.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	80.7	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	78.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	71.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	67.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	63.8	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	54.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	45.9	Fits
RTX 4070 12GB	12 GB	Q4_K_M	45.7	Heavy offload
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	42.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	37.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	28.7	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	10.9	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B60
Q3_K_S	3	4.4 GB	Low	B61
NVFP4	4	5.0 GB	Medium	B61
Q4_K_M	4	5.5 GB	Medium	B62
Q5_K_M	5	6.5 GB	High	B62
Q6_K	6	7.4 GB	High	B63
Q8_0Best for your GPU	8	9.6 GB	Very High	B65
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 2 9B on your machine.

Run

ollama run gemma2

Your hardware

More models your RX 7900 XT 20GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	39.6 tok/s
Qwen 3.5 27B	27B	A	17.8 tok/s
Qwen 3.6 27B	27B	S	22.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	A	42.1 tok/s
Magistral Small 2507	24B	S	25.7 tok/s

Frequently asked questions

Can RX 7900 XT 20GB run Gemma 2 9B?

Yes, RX 7900 XT 20GB can run Gemma 2 9B with a A grade (Runs well). Expected decode speed: 91.8 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 13.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 2 9B?

The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 2 9B run at on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Gemma 2 9B achieves approximately 91.8 tokens per second decode speed with a time-to-first-token of 2109ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RX 7900 XT 20GB receives a A grade with 91.8 tok/s and 8K context.

What context window can Gemma 2 9B use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Gemma 2 9B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for RX 7900 XT 20GB See all hardware for Gemma 2 9B

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