RTX 3060 12GB经济之选12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.48.7 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can DeepSeek R1 0528 Qwen3 8B run on RTX 5060 8GB?
YES — With Offload
C53Usable
Estimated from fit model
DeepSeek R1 0528 Qwen3 8B needs ~7.8 GB VRAM. RTX 5060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~56 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: 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) — 7.8 GB, 56.0 tok/s, Runs with offload
7.8 GB required8.0 GB available
98% VRAM used
Fit status
Runs with offload
Decode
56.0 tok/s
TTFT
3457 ms
Safe context
19K
Memory
7.8 GB / 8.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsDeepSeek R1 0528 Qwen3 8B on RTX 5060 8GB
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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 56.0 tok/s | 1886 ms | 19K |
| Coding | C | Runs with offload | 56.0 tok/s | 3457 ms | 19K |
| Agentic Coding | C | Very compromised (needs ~0.4 GB host RAM) | 35.6 tok/s | 7904 ms | 19K |
| Reasoning | C | Runs with offload | 56.0 tok/s | 4086 ms | 19K |
| RAG | C | Very compromised (needs ~0.4 GB host RAM) | 35.6 tok/s | 9880 ms | 19K |
Quantization options
How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on RTX 5060 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | C54 |
NVFP4 | 4 | 4.5 GB | Medium | C53 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
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-unsloth--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
RTX 3060 12GB经济之选RTX 5070 12GB最佳性价比12 GB VRAM (+4)672 GB/s (+224)
B
Raises estimated decode speed by about 55%.86.8 tok/s 解码
Raises estimated decode speed by about 55%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBNVIDIA 升级12 GB VRAM (+4)504 GB/s (+56)
B
Raises estimated decode speed by about 42%.79.5 tok/s 解码
Raises estimated decode speed by about 42%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Frequently asked questions
Can RTX 5060 8GB run DeepSeek R1 0528 Qwen3 8B?
Yes, RTX 5060 8GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Runs with offload). Expected decode speed: 56.0 tok/s.
How much VRAM does DeepSeek R1 0528 Qwen3 8B need?
DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 7.8 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 5060 8GB?
On RTX 5060 8GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
Can RTX 5060 8GB run DeepSeek R1 0528 Qwen3 8B for coding?
For coding workloads, DeepSeek R1 0528 Qwen3 8B on RTX 5060 8GB receives a C grade with 56.0 tok/s and 19K context.
What context window can DeepSeek R1 0528 Qwen3 8B use on RTX 5060 8GB?
On RTX 5060 8GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 19K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if DeepSeek R1 0528 Qwen3 8B feels slow on RTX 5060 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/hf-unsloth--deepseek-r1-0528-qwen3-8b-gguf-on-rtx-5060-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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