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URL: https://willitrunai.com/can-run/qwen-2.5-7b-on-radeon-ai-pro-r9700-32gb

⇱ Qwen 2.5 7B on Radeon AI PRO R9700 32GB? YES

Can Qwen 2.5 7B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

A74Great
○Estimated from fit model

Qwen 2.5 7B needs ~9.2 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~96 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
Share:

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) — 9.2 GB, 96.0 tok/s, Runs well
9.2 GB required32.0 GB available
29% VRAM used

Fit status

Runs well

Decode

96.0 tok/s

TTFT

2016 ms

Safe context

131K

Memory

9.2 GB / 32.0 GB

Memory breakdown

Weights4.3 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 7B 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: 96.0 tok/s decode · 2.0s TTFT (warm) · 240 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns well96.0 tok/s1100 ms131K
CodingARuns well96.0 tok/s2016 ms131K
Agentic CodingARuns well96.0 tok/s2933 ms131K
ReasoningARuns well96.0 tok/s2383 ms131K
RAGARuns well96.0 tok/s3666 ms131K

Quantization options

How Qwen 2.5 7B (7B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowB69
Q3_K_S
3
3.4 GB
LowB69
NVFP4
4
3.9 GB
MediumB69
Q4_K_M
4
4.3 GB
MediumB69
Q5_K_M
5
5.0 GB
HighB70
Q6_K
6
5.7 GB
HighB70
Q8_0
8
7.5 GB
Very HighA71
F16Best for your GPU
16
14.3 GB
MaximumA74

Get started

Copy-paste commands to run Qwen 2.5 7B on your machine.

Run

ollama run qwen2.5

Your hardware

More models your Radeon AI PRO R9700 32GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen3-Coder 30B A3B Instruct		30.5BS57.1 tok/s
👁 Alibaba
Qwen 3.5 27B		27BS24.8 tok/s
👁 Alibaba
Qwen 3.6 27B		27BS18.8 tok/s
👁 Alibaba
Qwen 3.6 35B A3B		35BS48 tok/s
👁 Alibaba
Qwen3-VL 30B A3B Instruct		30BS59.1 tok/s

Frequently asked questions

See all results for Radeon AI PRO R9700 32GBSee all hardware for Qwen 2.5 7B