logos16v2 stablelm2 1.6b i1 needs ~5.3 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~30 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
30.4 tok/s
TTFT
6368 ms
Safe context
2.3M
Memory
5.3 GB / 32.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 30.4 tok/s | 3474 ms | 2.2M |
| Coding | C | Runs well | 30.4 tok/s | 6368 ms | 2.3M |
| Agentic Coding | C | Runs well | 30.4 tok/s | 9263 ms | 2.3M |
| Reasoning | C | Runs well | 30.4 tok/s | 7526 ms | 2.3M |
| RAG | C | Runs well | 30.4 tok/s | 11579 ms | 2.3M |
How logos16v2 stablelm2 1.6b i1 (1.600000023841858B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | C42 |
Q3_K_S | 3 | 0.8 GB | Low | C42 |
NVFP4 | 4 | 0.9 GB | Medium | C42 |
Q4_K_M | 4 | 1.0 GB | Medium | C42 |
Q5_K_M | 5 | 1.2 GB | High | C42 |
Q6_K | 6 | 1.3 GB | High | C42 |
Q8_0 | 8 | 1.7 GB | Very High | C42 |
F16Best for your GPU | 16 | 3.3 GB | Maximum | C43 |
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 startYes, RTX 5090 32GB can run logos16v2 stablelm2 1.6b i1 with a C grade (Runs well). Expected decode speed: 30.4 tok/s.
logos16v2 stablelm2 1.6b i1 (1.600000023841858B parameters) requires approximately 5.3 GB of memory with Q4_K_M quantization.
The recommended quantization for logos16v2 stablelm2 1.6b i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 5090 32GB, logos16v2 stablelm2 1.6b i1 achieves approximately 30.4 tokens per second decode speed with a time-to-first-token of 6368ms using Q4_K_M quantization.
For coding workloads, logos16v2 stablelm2 1.6b i1 on RTX 5090 32GB receives a C grade with 30.4 tok/s and 2.3M context.
On RTX 5090 32GB, logos16v2 stablelm2 1.6b i1 can safely use up to 2.3M tokens of context. 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-rtx-5090-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: