This page answers Moonshotai Kimi Linear 48B A3b Instruct q2_k quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.
Short answer: Moonshotai Kimi Linear 48B A3b Instruct typically needs around 14GB VRAM at Q2_K, and 17GB is safer for smoother usage.
Estimated from Q4 using a 45% memory reduction assumption for Q2_K.
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 | 267 tok/s | View full compatibility | Buy options |
| NVIDIA H200 SXM 141GB | 141GB | Q4 | 241 tok/s | View full compatibility | Buy options |
| NVIDIA H100 SXM5 80GB | 80GB | Q4 | 173 tok/s | View full compatibility | Buy options |
| AMD Instinct MI250X | 128GB | Q4 | 167 tok/s | View full compatibility | Buy options |
| NVIDIA H100 PCIe 80GB | 80GB | Q4 | 110 tok/s | View full compatibility | Buy options |
| RTX 5090 | 32GB | Q4 | 105 tok/s | View full compatibility | Buy options |
| NVIDIA A100 80GB SXM4 | 80GB | Q4 | 102 tok/s | View full compatibility | Buy options |
| AMD Instinct MI210 | 64GB | Q4 | 83 tok/s | View full compatibility | Buy options |
| NVIDIA A100 40GB PCIe | 40GB | Q4 | 80 tok/s | View full compatibility | Buy options |
| RTX 4090 | 24GB | Q4 | 63 tok/s | View full compatibility | Buy options |
| NVIDIA RTX 6000 Ada | 48GB | Q4 | 62 tok/s | View full compatibility | Buy options |
| NVIDIA L40 | 48GB | Q4 | 58 tok/s | View full compatibility | Buy options |
Moonshotai Kimi Linear 48B A3b Instruct at Q2_K is estimated to require about 14GB VRAM minimum, with 17GB 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.
Q2_K is a balance point between memory usage and quality. If your GPU is below 14GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.