Can DeepSeek R1 0528 Qwen3 8B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

C48Usable

Estimated from fit model

DeepSeek R1 0528 Qwen3 8B needs ~9.9 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~77 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) — 9.9 GB, 77.4 tok/s, Runs well

9.9 GB required32.0 GB available

31% VRAM used

Fit status

Runs well

Decode

77.4 tok/s

TTFT

2502 ms

Safe context

393K

Memory

9.9 GB / 32.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsDeepSeek R1 0528 Qwen3 8B on Radeon AI PRO R9700 32GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 77.4 tok/s decode · 2.5s TTFT (warm) · 193 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	77.4 tok/s	1365 ms	393K
Coding	C	Runs well	77.4 tok/s	2502 ms	393K
Agentic Coding	C	Runs well	77.4 tok/s	3639 ms	393K
Reasoning	C	Runs well	77.4 tok/s	2957 ms	393K
RAG	C	Runs well	77.4 tok/s	4549 ms	393K

Quantization options

How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C43
Q3_K_S	3	3.9 GB	Low	C43
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 0528 Qwen3 8B on your machine.

Run

lms load hf-maziyarpanahi--deepseek-r1-0528-qwen3-8b-gguf && lms server start

Upgrade options

Hardware that runs DeepSeek R1 0528 Qwen3 8B well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.76.8 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 64GBBest value

64 GB Unified (+32)800 GB/s (+160)

Adds memory headroom for longer context windows and future model growth.95.1 tok/s decode

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

~$3,999 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run DeepSeek R1 0528 Qwen3 8B?

Yes, Radeon AI PRO R9700 32GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Runs well). Expected decode speed: 77.4 tok/s.

How much VRAM does DeepSeek R1 0528 Qwen3 8B need?

DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 9.9 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 0528 Qwen3 8B?

The recommended quantization for DeepSeek R1 0528 Qwen3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 0528 Qwen3 8B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 77.4 tokens per second decode speed with a time-to-first-token of 2502ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run DeepSeek R1 0528 Qwen3 8B for coding?

For coding workloads, DeepSeek R1 0528 Qwen3 8B on Radeon AI PRO R9700 32GB receives a C grade with 77.4 tok/s and 393K context.

What context window can DeepSeek R1 0528 Qwen3 8B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 393K 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 DeepSeek R1 0528 Qwen3 8B

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