MacBook Pro M4 Max 96GBOpção econômica
96 GB Unified (+32)
C
This setup is broadly balanced for this model.76.8 tok/s decodificação
~$2,499 MSRP
Can DeepSeek R1 0528 Qwen3 8B run on NVIDIA A16 64GB?
YES — Runs Great
C46Usable
Estimated from fit model
DeepSeek R1 0528 Qwen3 8B needs ~13.4 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~96 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 13.4 GB, 95.9 tok/s, Runs well
13.4 GB required64.0 GB available
21% VRAM used
Fit status
Runs well
Decode
95.9 tok/s
TTFT
2019 ms
Safe context
879K
Memory
13.4 GB / 64.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsDeepSeek R1 0528 Qwen3 8B on NVIDIA A16 64GB
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: 95.9 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
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 95.9 tok/s | 1101 ms | 879K |
| Coding | C | Runs well | 95.9 tok/s | 2019 ms | 879K |
| Agentic Coding | C | Runs well | 95.9 tok/s | 2936 ms | 879K |
| Reasoning | C | Runs well | 95.9 tok/s | 2386 ms | 879K |
| RAG | C | Runs well | 95.9 tok/s | 3670 ms | 879K |
Quantization options
How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C40 |
Q3_K_S | 3 | 3.9 GB | Low | C40 |
NVFP4 | 4 | 4.5 GB | Medium | C41 |
Q4_K_M | 4 | 4.9 GB | Medium | C41 |
Q5_K_M | 5 | 5.8 GB | High | C41 |
Q6_K | 6 | 6.6 GB | High | C41 |
Q8_0 | 8 | 8.6 GB | Very High | C41 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C43 |
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 startOpções de upgrade
Hardware que roda bem DeepSeek R1 0528 Qwen3 8B
Frequently asked questions
Can NVIDIA A16 64GB run DeepSeek R1 0528 Qwen3 8B?
Yes, NVIDIA A16 64GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Runs well). Expected decode speed: 95.9 tok/s.
How much VRAM does DeepSeek R1 0528 Qwen3 8B need?
DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 13.4 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 NVIDIA A16 64GB?
On NVIDIA A16 64GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 95.9 tokens per second decode speed with a time-to-first-token of 2019ms using Q4_K_M quantization.
Can NVIDIA A16 64GB run DeepSeek R1 0528 Qwen3 8B for coding?
For coding workloads, DeepSeek R1 0528 Qwen3 8B on NVIDIA A16 64GB receives a C grade with 95.9 tok/s and 879K context.
What context window can DeepSeek R1 0528 Qwen3 8B use on NVIDIA A16 64GB?
On NVIDIA A16 64GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 879K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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