NVIDIA H200 141GB经济之选141 GB VRAM (+13)4800 GB/s (+4527)
B
Raises estimated decode speed by about 779%.182 tok/s 解码
Raises estimated decode speed by about 779%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Can WizardLM 13B run on NVIDIA DGX Spark 128GB?
YES — With F16
B65Good
Estimated from fit model
WizardLM 13B needs ~53.1 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~9 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.
WizardLM 13B at Q4_K_M needs 21.3 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (53.1 GB) with maximum quality. 8 quantization levels fit.
Select quantization to explore
Q4_K_M (Medium quality) — 34.4 GB, 20.7 tok/s, Runs well
34.4 GB required108.8 GB available
32% VRAM used
Fit status
Runs well
Decode
20.7 tok/s
TTFT
9373 ms
Safe context
8K
Memory
34.4 GB / 108.8 GB
Memory breakdown
Weights7.9 GB
KV Cache12.2 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsWizardLM 13B 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: 20.7 tok/s decode · 9.4s TTFT (warm) · 52 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 | B | Runs well | 20.7 tok/s | 5112 ms | 8K |
| Coding | F | Too heavy | 3.7 tok/s | 52071 ms | 4K |
| Agentic Coding | B | Runs well | 20.7 tok/s | 13633 ms | 8K |
| Reasoning | B | Runs well | 20.7 tok/s | 11077 ms | 8K |
| RAG | B | Runs well | 20.7 tok/s | 17041 ms | 8K |
Quantization options
How WizardLM 13B (13B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B60 |
Q3_K_S | 3 | 6.4 GB | Low | B60 |
NVFP4 | 4 | 7.3 GB | Medium | B60 |
Q4_K_M | 4 | 7.9 GB | Medium | B60 |
Q5_K_M | 5 | 9.4 GB | High | B60 |
Q6_K | 6 | 10.7 GB | High | B60 |
Q8_0 | 8 | 13.9 GB | Very High | B61 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B62 |
Get started
Copy-paste commands to run WizardLM 13B on your machine.
Run
lms load WizardLM-13B-V1.0 && lms server start升级选项
能流畅运行 WizardLM 13B 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)
B
Raises estimated decode speed by about 779%.182 tok/s 解码
Raises estimated decode speed by about 779%.
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)
B
Raises estimated decode speed by about 779%.182 tok/s 解码
Raises estimated decode speed by about 779%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Frequently asked questions
Can NVIDIA DGX Spark 128GB run WizardLM 13B?
Yes, NVIDIA DGX Spark 128GB can run WizardLM 13B at F16 quantization (Runs well). The recommended Q4_K_M requires 21.3 GB which exceeds available memory, but at F16 it needs only 53.1 GB. Expected decode speed: 8.6 tok/s.
How much VRAM does WizardLM 13B need?
WizardLM 13B (13B parameters) requires approximately 21.3 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 53.1 GB.
What is the best quantization for WizardLM 13B?
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 53.1 GB.
What speed will WizardLM 13B run at on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, WizardLM 13B achieves approximately 8.6 tokens per second decode speed with a time-to-first-token of 22499ms using F16 quantization.
Can NVIDIA DGX Spark 128GB run WizardLM 13B for coding?
For coding workloads, WizardLM 13B on NVIDIA DGX Spark 128GB receives a F grade with 3.7 tok/s and 4K context.
What context window can WizardLM 13B use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, WizardLM 13B can safely use up to 8K tokens of context at F16 quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.
Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for WizardLM 13B?
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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