MacBook Pro M4 Max 48GB经济之选
48 GB Unified (+16)
C
This setup is broadly balanced for this model.76.8 tok/s 解码
~$2,499 MSRP
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.
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 | 4.5 GB | Medium | C44 |
Q4_K_M | 4 | 4.9 GB | Medium | C44 |
Q5_K_M | 5 | 5.8 GB | High | C44 |
Q6_K | 6 | 6.6 GB | High | C44 |
Q8_0 | 8 | 8.6 GB | Very High | C45 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C49 |
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升级选项
能流畅运行 DeepSeek R1 0528 Qwen3 8B 的硬件
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.
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