NVIDIA H200 141GBコスパ重視141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 233%.112 tok/sデコード
Raises estimated decode speed by about 233%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Can DeepSeek R1 0528 Qwen3 8B run on NVIDIA DGX Spark 128GB?
YES — Runs Great
C43Usable
Estimated from fit model
DeepSeek R1 0528 Qwen3 8B needs ~20.1 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~34 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) — 20.1 GB, 33.6 tok/s, Runs well
20.1 GB required108.8 GB available
18% VRAM used
Fit status
Runs well
Decode
33.6 tok/s
TTFT
5768 ms
Safe context
1.5M
Memory
20.1 GB / 108.8 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsDeepSeek R1 0528 Qwen3 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: 33.6 tok/s decode · 5.8s TTFT (warm) · 84 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 | C | Runs well | 33.6 tok/s | 3146 ms | 1.5M |
| Coding | C | Runs well | 33.6 tok/s | 5768 ms | 1.5M |
| Agentic Coding | C | Runs well | 33.6 tok/s | 8390 ms | 1.5M |
| Reasoning | C | Runs well | 33.6 tok/s | 6817 ms | 1.5M |
| RAG | C | Runs well | 33.6 tok/s | 10487 ms | 1.5M |
Quantization options
How DeepSeek R1 0528 Qwen3 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 | D39 |
Q3_K_S | 3 | 3.9 GB | Low | D39 |
NVFP4 | 4 | 4.5 GB | Medium | D39 |
Q4_K_M | 4 | 4.9 GB | Medium | D39 |
Q5_K_M | 5 | 5.8 GB | High | D40 |
Q6_K | 6 | 6.6 GB | High | D40 |
Q8_0 | 8 | 8.6 GB | Very High | D40 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C41 |
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アップグレードオプション
DeepSeek R1 0528 Qwen3 8Bを快適に動かすハードウェア
NVIDIA H200 141GBコスパ重視NVIDIA H200 PCIe 141GBコスパ最良141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 233%.112 tok/sデコード
Raises estimated decode speed by about 233%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
NVIDIA B200 180GBNVIDIAアップグレード180 GB VRAM (+52)8000 GB/s (+7727)
C
Raises estimated decode speed by about 233%.112 tok/sデコード
Raises estimated decode speed by about 233%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Frequently asked questions
Can NVIDIA DGX Spark 128GB run DeepSeek R1 0528 Qwen3 8B?
Yes, NVIDIA DGX Spark 128GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Runs well). Expected decode speed: 33.6 tok/s.
How much VRAM does DeepSeek R1 0528 Qwen3 8B need?
DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 20.1 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 DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 33.6 tokens per second decode speed with a time-to-first-token of 5768ms using Q4_K_M quantization.
Can NVIDIA DGX Spark 128GB run DeepSeek R1 0528 Qwen3 8B for coding?
For coding workloads, DeepSeek R1 0528 Qwen3 8B on NVIDIA DGX Spark 128GB receives a C grade with 33.6 tok/s and 1.5M context.
What context window can DeepSeek R1 0528 Qwen3 8B use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 1.5M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for DeepSeek R1 0528 Qwen3 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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