Can gemma 2 2b it run on Radeon AI PRO R9700 32GB?

YES — Runs Great

C42Usable

Estimated from fit model

gemma 2 2b it needs ~5.6 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 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) — 5.6 GB, 28.0 tok/s, Runs well

5.6 GB required32.0 GB available

18% VRAM used

Fit status

Runs well

Decode

28.0 tok/s

TTFT

6914 ms

Safe context

1.8M

Memory

5.6 GB / 32.0 GB

Memory breakdown

Weights1.2 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsgemma 2 2b 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: 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	1.8M
Coding	C	Runs well	28.0 tok/s	6914 ms	1.8M
Agentic Coding	C	Runs well	28.0 tok/s	10057 ms	1.8M
Reasoning	C	Runs well	28.0 tok/s	8171 ms	1.8M
RAG	C	Runs well	28.0 tok/s	12571 ms	1.8M

Inference speed

gemma 2 2b it inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for gemma 2 2b it at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~38 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	38.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	32.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	32.0

Quantization options

How gemma 2 2b it (2B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.8 GB	Low	C43
Q3_K_S	3	1.0 GB	Low	C43
NVFP4	4

Get started

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

Run

lms load hf-maziyarpanahi--gemma-2-2b-it-gguf && lms server start

Upgrade options

Hardware that runs gemma 2 2b it well

Mac mini M4 64GBBudget pick

64 GB Unified (+32)

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.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

64 GB Unified (+32)

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.

~$1,599 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run gemma 2 2b it?

Yes, Radeon AI PRO R9700 32GB can run gemma 2 2b it with a C grade (Runs well). Expected decode speed: 28.0 tok/s.

How much VRAM does gemma 2 2b it need?

gemma 2 2b it (2B parameters) requires approximately 5.6 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 2 2b it?

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

What speed will gemma 2 2b it run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, gemma 2 2b it achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6914ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run gemma 2 2b it for coding?

For coding workloads, gemma 2 2b it on Radeon AI PRO R9700 32GB receives a C grade with 28.0 tok/s and 1.8M context.

What context window can gemma 2 2b it use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, gemma 2 2b it can safely use up to 1.8M 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 2 2b it

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