Can gemma 2b run on Radeon RX 7900M 16GB?

YES — Runs Great

C44Usable

Estimated from fit model

gemma 2b needs ~4.0 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~28 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) — 4.0 GB, 28.0 tok/s, Runs well

4.0 GB required16.0 GB available

25% VRAM used

Fit status

Runs well

Decode

28.0 tok/s

TTFT

6914 ms

Safe context

838K

Memory

4.0 GB / 16.0 GB

Memory breakdown

Weights1.2 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsgemma 2b on Radeon RX 7900M 16GB
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: 28.0 tok/s decode · 6.9s TTFT (warm) · 70 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	28.0 tok/s	3771 ms	838K
Coding	C	Runs well	28.0 tok/s	6914 ms	838K
Agentic Coding	C	Runs well	28.0 tok/s	10057 ms	838K
Reasoning	C	Runs well	28.0 tok/s	8171 ms	838K
RAG	C	Runs well	28.0 tok/s	12571 ms	838K

Quantization options

How gemma 2b (2B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.8 GB	Low	C46
Q3_K_S	3	1.0 GB	Low	C46
NVFP4	4

Get started

Copy-paste commands to run gemma 2b on your machine.

Run

lms load hf-google--gemma-2b && lms server start

Upgrade options

Hardware that runs gemma 2b well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+16)

Adds memory headroom for longer context windows and future model growth.28 tok/s decode

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

~$799 MSRP

MacBook Pro M3 24GBBest value

24 GB Unified (+8)

This setup is broadly balanced for this model.28 tok/s decode

~$1,099 MSRP

Frequently asked questions

Can Radeon RX 7900M 16GB run gemma 2b?

Yes, Radeon RX 7900M 16GB can run gemma 2b with a C grade (Runs well). Expected decode speed: 28.0 tok/s.

How much VRAM does gemma 2b need?

gemma 2b (2B parameters) requires approximately 4.0 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 2b?

The recommended quantization for gemma 2b is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 2b run at on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, gemma 2b achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6914ms using Q4_K_M quantization.

Can Radeon RX 7900M 16GB run gemma 2b for coding?

For coding workloads, gemma 2b on Radeon RX 7900M 16GB receives a C grade with 28.0 tok/s and 838K context.

What context window can gemma 2b use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, gemma 2b can safely use up to 838K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon RX 7900M 16GB See all hardware for gemma 2b

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-google--gemma-2b-on-rx-7900m-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: