NVIDIA H200 141GBコスパ重視141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 88%.84 tok/sデコード
Raises estimated decode speed by about 88%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Can stablelm 2 zephyr 1 6b run on NVIDIA DGX Spark 128GB?
YES — Runs Great
C43Usable
Estimated from fit model
stablelm 2 zephyr 1 6b needs ~18.6 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~45 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) — 18.6 GB, 44.8 tok/s, Runs well
18.6 GB required108.8 GB available
17% VRAM used
Fit status
Runs well
Decode
44.8 tok/s
TTFT
4326 ms
Safe context
2.1M
Memory
18.6 GB / 108.8 GB
Memory breakdown
Weights3.7 GB
KV Cache0.7 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsstablelm 2 zephyr 1 6b on NVIDIA DGX Spark 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 44.8 tok/s decode · 4.3s TTFT (warm) · 112 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 | 44.8 tok/s | 2360 ms | 2.1M |
| Coding | C | Runs well | 44.8 tok/s | 4326 ms | 2.1M |
| Agentic Coding | C | Runs well | 44.8 tok/s | 6292 ms | 2.1M |
| Reasoning | C | Runs well | 44.8 tok/s | 5112 ms | 2.1M |
| RAG | F | Too heavy | 8.1 tok/s | 43696 ms | 4K |
Quantization options
How stablelm 2 zephyr 1 6b (6B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | D39 |
Q3_K_S | 3 | 2.9 GB | Low | D39 |
NVFP4 | 4 | 3.4 GB | Medium | D39 |
Q4_K_M | 4 | 3.7 GB | Medium | D39 |
Q5_K_M | 5 | 4.3 GB | High | D39 |
Q6_K | 6 | 4.9 GB | High | D39 |
Q8_0 | 8 | 6.4 GB | Very High | D39 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | D40 |
Get started
Copy-paste commands to run stablelm 2 zephyr 1 6b on your machine.
Run
lms load hf-stabilityai--stablelm-2-zephyr-1-6b && lms server startアップグレードオプション
stablelm 2 zephyr 1 6b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 88%.84 tok/sデコード
Raises estimated decode speed by about 88%.
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 88%.84 tok/sデコード
Raises estimated decode speed by about 88%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Frequently asked questions
Can NVIDIA DGX Spark 128GB run stablelm 2 zephyr 1 6b?
Yes, NVIDIA DGX Spark 128GB can run stablelm 2 zephyr 1 6b with a C grade (Runs well). Expected decode speed: 44.8 tok/s.
How much VRAM does stablelm 2 zephyr 1 6b need?
stablelm 2 zephyr 1 6b (6B parameters) requires approximately 18.6 GB of memory with Q4_K_M quantization.
What is the best quantization for stablelm 2 zephyr 1 6b?
The recommended quantization for stablelm 2 zephyr 1 6b is Q4_K_M, which balances quality and memory efficiency.
What speed will stablelm 2 zephyr 1 6b run at on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, stablelm 2 zephyr 1 6b achieves approximately 44.8 tokens per second decode speed with a time-to-first-token of 4326ms using Q4_K_M quantization.
Can NVIDIA DGX Spark 128GB run stablelm 2 zephyr 1 6b for coding?
For coding workloads, stablelm 2 zephyr 1 6b on NVIDIA DGX Spark 128GB receives a C grade with 44.8 tok/s and 2.1M context.
What context window can stablelm 2 zephyr 1 6b use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, stablelm 2 zephyr 1 6b can safely use up to 2.1M 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 stablelm 2 zephyr 1 6b?
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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