logos16v2 stablelm2 1.6b i1 needs ~17.7 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~22 tok/s.
Operating mode
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
Fit status
Runs well
Decode
22.4 tok/s
TTFT
8643 ms
Safe context
8.0M
Memory
15.4 GB / 108.8 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 22.4 tok/s | 4714 ms | 4K |
| Coding | F | Too heavy | 22.4 tok/s | 8643 ms | 4K |
| Agentic Coding | F | Too heavy | 22.4 tok/s | 12571 ms | 4K |
| Reasoning | F | Too heavy | 22.4 tok/s | 10214 ms | 4K |
| RAG | F | Too heavy | 22.4 tok/s | 15714 ms | 4K |
How logos16v2 stablelm2 1.6b i1 (1.600000023841858B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | D39 |
Q3_K_S | 3 | 0.8 GB | Low | D39 |
NVFP4 | 4 |
Copy-paste commands to run logos16v2 stablelm2 1.6b i1 on your machine.
Run
lms load hf-mradermacher--logos16v2-stablelm2-1-6b-i1-gguf && lms server startUpgrade options
Yes, NVIDIA DGX Spark 128GB can run logos16v2 stablelm2 1.6b i1 at F16 quantization (Runs well). The recommended Q4_K_M requires 2.4 GB which exceeds available memory, but at F16 it needs only 17.7 GB. Expected decode speed: 22.4 tok/s.
logos16v2 stablelm2 1.6b i1 (1.600000023841858B parameters) requires approximately 2.4 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 17.7 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 17.7 GB.
On NVIDIA DGX Spark 128GB, logos16v2 stablelm2 1.6b i1 achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8643ms using F16 quantization.
For coding workloads, logos16v2 stablelm2 1.6b i1 on NVIDIA DGX Spark 128GB receives a F grade with 22.4 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, logos16v2 stablelm2 1.6b i1 can safely use up to 7.8M tokens of context at F16 quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--logos16v2-stablelm2-1-6b-i1-gguf-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>
Preview:
0.9 GB |
| Medium |
| D39 |
Q4_K_M | 4 | 1.0 GB | Medium | D39 |
Q5_K_M | 5 | 1.2 GB | High | D39 |
Q6_K | 6 | 1.3 GB | High | D39 |
Q8_0 | 8 | 1.7 GB | Very High | D39 |
F16Best for your GPU | 16 | 3.3 GB | Maximum | D39 |
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.