NVIDIA H200 141GB经济之选141 GB VRAM (+13)4800 GB/s (+4527)
S
Raises estimated decode speed by about 210%.112 tok/s 解码
Raises estimated decode speed by about 210%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Can Qwen 3 8B run on NVIDIA DGX Spark 128GB?
YES — With F16
A82Great
Estimated from fit model
Qwen 3 8B needs ~32.9 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~15 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.
Qwen 3 8B at Q4_K_M needs 8.3 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (32.9 GB) with maximum quality. 8 quantization levels fit.
Select quantization to explore
Q4_K_M (Medium quality) — 21.3 GB, 36.1 tok/s, Runs well
21.3 GB required108.8 GB available
20% VRAM used
Fit status
Runs well
Decode
36.1 tok/s
TTFT
5365 ms
Safe context
131K
Memory
21.3 GB / 108.8 GB
Memory breakdown
Weights4.9 GB
KV Cache2.2 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsQwen 3 8B on NVIDIA DGX Spark 128GB
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: 36.1 tok/s decode · 5.4s TTFT (warm) · 90 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 6.0 tok/s | 17478 ms | 4K |
| Coding | F | Too heavy | 6.0 tok/s | 32043 ms | 4K |
| Agentic Coding | F | Too heavy | 6.0 tok/s | 46609 ms | 4K |
| Reasoning | F | Too heavy | 6.0 tok/s | 37869 ms | 4K |
| RAG | F | Too heavy | 6.0 tok/s | 58261 ms | 4K |
Quantization options
How Qwen 3 8B (8B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A79 |
Q3_K_S | 3 | 3.9 GB | Low | A79 |
NVFP4 | 4 | 4.5 GB | Medium | A79 |
Q4_K_M | 4 | 4.9 GB | Medium | A79 |
Q5_K_M | 5 | 5.8 GB | High | A79 |
Q6_K | 6 | 6.6 GB | High | A79 |
Q8_0 | 8 | 8.6 GB | Very High | A79 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | A80 |
Get started
Copy-paste commands to run Qwen 3 8B on your machine.
Run
ollama run qwen3:8b升级选项
能流畅运行 Qwen 3 8B 的硬件
NVIDIA H200 141GB经济之选NVIDIA H200 PCIe 141GB最佳性价比141 GB VRAM (+13)4800 GB/s (+4527)
S
Raises estimated decode speed by about 210%.112 tok/s 解码
Raises estimated decode speed by about 210%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBNVIDIA 升级180 GB VRAM (+52)8000 GB/s (+7727)
A
Raises estimated decode speed by about 210%.112 tok/s 解码
Raises estimated decode speed by about 210%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Frequently asked questions
Can NVIDIA DGX Spark 128GB run Qwen 3 8B?
Yes, NVIDIA DGX Spark 128GB can run Qwen 3 8B at F16 quantization (Runs well). The recommended Q4_K_M requires 8.3 GB which exceeds available memory, but at F16 it needs only 32.9 GB. Expected decode speed: 15.0 tok/s.
How much VRAM does Qwen 3 8B need?
Qwen 3 8B (8B parameters) requires approximately 8.3 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 32.9 GB.
What is the best quantization for Qwen 3 8B?
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 32.9 GB.
What speed will Qwen 3 8B run at on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, Qwen 3 8B achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12879ms using F16 quantization.
Can NVIDIA DGX Spark 128GB run Qwen 3 8B for coding?
For coding workloads, Qwen 3 8B on NVIDIA DGX Spark 128GB receives a F grade with 6.0 tok/s and 4K context.
What context window can Qwen 3 8B use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, Qwen 3 8B can safely use up to 131K tokens of context at F16 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.
Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Qwen 3 8B?
Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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<iframe src="https://willitrunai.com/embed/qwen-3-8b-on-dgx-spark-128gb" 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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