Can gemma 3 27b it run on Radeon AI PRO R9700 32GB?

YES — Runs Great

C52Usable

Estimated from fit model

gemma 3 27b it needs ~23.7 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~23 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) — 23.7 GB, 22.9 tok/s, Runs well

23.7 GB required32.0 GB available

74% VRAM used

Fit status

Runs well

Decode

22.9 tok/s

TTFT

8444 ms

Safe context

58K

Memory

23.7 GB / 32.0 GB

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsgemma 3 27b it on Radeon AI PRO R9700 32GB
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: 22.9 tok/s decode · 8.4s TTFT (warm) · 57 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	22.9 tok/s	4606 ms	58K
Coding	C	Runs well	22.9 tok/s	8444 ms	58K
Agentic Coding	C	Tight fit	22.9 tok/s	12283 ms	58K
Reasoning	C	Runs well	22.9 tok/s	9980 ms	58K
RAG	C	Tight fit	22.9 tok/s	15353 ms	58K

Quantization options

How gemma 3 27b it (27B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	C46
Q3_K_S	3	13.2 GB	Low	C48
NVFP4	4

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

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

Raises estimated decode speed by about 46%.33.4 tok/s decode

Raises estimated decode speed by about 46%.

~$2,499 MSRP

NVIDIA A100 40GBBest value

40 GB VRAM (+8)1555 GB/s (+915)

Raises estimated decode speed by about 246%.79.3 tok/s decode

Raises estimated decode speed by about 246%.

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

~$10,000 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run gemma 3 27b it?

Yes, Radeon AI PRO R9700 32GB can run gemma 3 27b it with a C grade (Runs well). Expected decode speed: 22.9 tok/s.

How much VRAM does gemma 3 27b it need?

gemma 3 27b it (27B parameters) requires approximately 23.7 GB of memory with Q4_K_M quantization.

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

The recommended quantization for gemma 3 27b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 27b it run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, gemma 3 27b it achieves approximately 22.9 tokens per second decode speed with a time-to-first-token of 8444ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run gemma 3 27b it for coding?

For coding workloads, gemma 3 27b it on Radeon AI PRO R9700 32GB receives a C grade with 22.9 tok/s and 58K context.

What context window can gemma 3 27b it use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, gemma 3 27b it can safely use up to 58K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for gemma 3 27b it

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--gemma-3-27b-it-gguf-on-radeon-ai-pro-r9700-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: