RTX 3090 24GBOpção econômica24 GB VRAM (+4)936 GB/s (+576)
C
Raises estimated decode speed by about 95%.112 tok/s decodificação
Raises estimated decode speed by about 95%.
~$1,499 MSRP
Can DeepSeek R1 0528 Qwen3 8B run on RTX 4000 Ada 20GB?
YES — Runs Great
C50Usable
Estimated from fit model
DeepSeek R1 0528 Qwen3 8B needs ~9.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~58 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 9.0 GB, 57.5 tok/s, Runs well
9.0 GB required20.0 GB available
45% VRAM used
Fit status
Runs well
Decode
57.5 tok/s
TTFT
3365 ms
Safe context
203K
Memory
9.0 GB / 20.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsDeepSeek R1 0528 Qwen3 8B on RTX 4000 Ada 20GB
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: 57.5 tok/s decode · 3.4s TTFT (warm) · 144 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 | 57.5 tok/s | 1835 ms | 203K |
| Coding | C | Runs well | 57.5 tok/s | 3365 ms | 203K |
| Agentic Coding | C | Runs well | 57.5 tok/s | 4894 ms | 203K |
| Reasoning | C | Runs well | 57.5 tok/s | 3976 ms | 203K |
| RAG | C | Runs well | 57.5 tok/s | 6117 ms | 203K |
Quantization options
How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C46 |
Q3_K_S | 3 | 3.9 GB | Low | C46 |
NVFP4 | 4 | 4.5 GB | Medium | C47 |
Q4_K_M | 4 | 4.9 GB | Medium | C47 |
Q5_K_M | 5 | 5.8 GB | High | C48 |
Q6_K | 6 | 6.6 GB | High | C48 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | C50 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run DeepSeek R1 0528 Qwen3 8B on your machine.
Run
lms load hf-lmstudio-community--deepseek-r1-0528-qwen3-8b-gguf && lms server startOpções de upgrade
Hardware que roda bem DeepSeek R1 0528 Qwen3 8B
RTX 3090 24GBOpção econômicaRTX 4090 24GBMelhor custo-benefício24 GB VRAM (+4)1008 GB/s (+648)
C
Raises estimated decode speed by about 95%.112 tok/s decodificação
Raises estimated decode speed by about 95%.
~$1,599 MSRP
RTX PRO 4000 Blackwell 24GBUpgrade NVIDIA24 GB VRAM (+4)672 GB/s (+312)
C
Raises estimated decode speed by about 95%.112 tok/s decodificação
Raises estimated decode speed by about 95%.
~$1,599 MSRP
Frequently asked questions
Can RTX 4000 Ada 20GB run DeepSeek R1 0528 Qwen3 8B?
Yes, RTX 4000 Ada 20GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Runs well). Expected decode speed: 57.5 tok/s.
How much VRAM does DeepSeek R1 0528 Qwen3 8B need?
DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 9.0 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 RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 57.5 tokens per second decode speed with a time-to-first-token of 3365ms using Q4_K_M quantization.
Can RTX 4000 Ada 20GB run DeepSeek R1 0528 Qwen3 8B for coding?
For coding workloads, DeepSeek R1 0528 Qwen3 8B on RTX 4000 Ada 20GB receives a C grade with 57.5 tok/s and 203K context.
What context window can DeepSeek R1 0528 Qwen3 8B use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 203K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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