Can gemma 3 27b it run on Radeon RX 7900M 16GB?

Q: What should I upgrade first if gemma 3 27b it feels slow on Radeon RX 7900M 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

D32Poor

Estimated from fit model

gemma 3 27b it needs ~18.9 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q3_K_S quantization, expect ~13 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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.

gemma 3 27b it at Q4_K_M needs 22.1 GB — too much for Radeon RX 7900M 16GB (16.0 GB). Runs at Q3_K_S (18.9 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.1 GB, exceeds 16.0 GB available

22.1 GB required16.0 GB available

138% VRAM needed

6.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.8 tok/s

TTFT

24771 ms

Safe context

Memory

22.1 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsgemma 3 27b it 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: 7.8 tok/s decode · 24.8s TTFT (warm) · 20 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	9.1 tok/s	11559 ms	4K
Coding	F	Too heavy	7.8 tok/s	24771 ms	4K
Agentic Coding	F	Too heavy	5.9 tok/s	47733 ms	4K
Reasoning	F	Too heavy	7.8 tok/s	29275 ms	4K
RAG	F	Too heavy	5.9 tok/s	59666 ms	4K

Quantization options

How gemma 3 27b it (27B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	10.5 GB	Low	C51
Q3_K_S	3	13.2 GB	Low	F0

Get started

Copy-paste commands to run gemma 3 27b it on your machine.

Run

lms load hf-maziyarpanahi--gemma-3-27b-it-gguf && lms server start

Upgrade options

Hardware that runs gemma 3 27b it well

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+224)

Makes the model fit on the accelerator instead of staying completely out of reach.17 tok/s decode

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

Raises estimated decode speed by about 118%.

~$899 MSRP

RX 7900 XTX 24GBBudget pick

24 GB VRAM (+8)960 GB/s (+384)

Makes the model fit on the accelerator instead of staying completely out of reach.42 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.

~$999 MSRP

Radeon AI PRO R9700 32GBAMD upgrade

32 GB VRAM (+16)640 GB/s (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.22.9 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.

~$1,899 MSRP

Frequently asked questions

Can Radeon RX 7900M 16GB run gemma 3 27b it?

Yes, Radeon RX 7900M 16GB can run gemma 3 27b it at Q3_K_S quantization (Very compromised (needs ~2 GB host RAM)). The recommended Q4_K_M requires 22.1 GB which exceeds available memory, but at Q3_K_S it needs only 18.9 GB. Expected decode speed: 12.6 tok/s.

How much VRAM does gemma 3 27b it need?

gemma 3 27b it (27B parameters) requires approximately 22.1 GB at Q4_K_M quantization. On Radeon RX 7900M 16GB, it fits at Q3_K_S using 18.9 GB.

What is the best quantization for gemma 3 27b it?

The recommended quantization is Q4_K_M, but on Radeon RX 7900M 16GB the best fitting quantization is Q3_K_S, which uses 18.9 GB.

What speed will gemma 3 27b it run at on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, gemma 3 27b it achieves approximately 12.6 tokens per second decode speed with a time-to-first-token of 15333ms using Q3_K_S quantization.

Can Radeon RX 7900M 16GB run gemma 3 27b it for coding?

For coding workloads, gemma 3 27b it on Radeon RX 7900M 16GB receives a F grade with 7.8 tok/s and 4K context.

What context window can gemma 3 27b it use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, gemma 3 27b it can safely use up to 4K tokens of context at Q3_K_S quantization. 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 3 27b it

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