This page answers Apple Openelm 1 1B Instruct q3_k_s quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.
Short answer: Apple Openelm 1 1B Instruct typically needs around 1GB VRAM at Q3_K_S, and 2GB is safer for smoother usage.
Estimated from Q4 using a 28% memory reduction assumption for Q3_K_S.
Throughput data below uses available compatibility measurements/estimates and is sorted by tokens per second for this model.
Need general guidance? Review full methodology.
| GPU | VRAM | Quantization | Speed | Compatibility | Buy |
|---|---|---|---|---|---|
| AMD Instinct MI300X | 192GB | Q4 | 916 tok/s | View full compatibility | Buy options |
| NVIDIA H200 SXM 141GB | 141GB | Q4 | 827 tok/s | View full compatibility | Buy options |
| NVIDIA H100 SXM5 80GB | 80GB | Q4 | 594 tok/s | View full compatibility | Buy options |
| AMD Instinct MI250X | 128GB | Q4 | 573 tok/s | View full compatibility | Buy options |
| NVIDIA H100 PCIe 80GB | 80GB | Q4 | 377 tok/s | View full compatibility | Buy options |
| RTX 5090 | 32GB | Q4 | 360 tok/s | View full compatibility | Buy options |
| NVIDIA A100 80GB SXM4 | 80GB | Q4 | 350 tok/s | View full compatibility | Buy options |
| AMD Instinct MI210 | 64GB | Q4 | 285 tok/s | View full compatibility | Buy options |
| NVIDIA A100 40GB PCIe | 40GB | Q4 | 273 tok/s | View full compatibility | Buy options |
| RTX 4090 | 24GB | Q4 | 216 tok/s | View full compatibility | Buy options |
| NVIDIA RTX 6000 Ada | 48GB | Q4 | 214 tok/s | View full compatibility | Buy options |
| NVIDIA L40 | 48GB | Q4 | 199 tok/s | View full compatibility | Buy options |
Apple Openelm 1 1B Instruct at Q3_K_S is estimated to require about 1GB VRAM minimum, with 2GB recommended for smoother operation.
Start with AMD Instinct MI300X, NVIDIA H200 SXM 141GB, NVIDIA H100 SXM5 80GB and review each compatibility page for full speed and fit details.
Q3_K_S is a balance point between memory usage and quality. If your GPU is below 1GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.