Afrideva
stablelm 3b 4e1t (3B parameters) requires approximately 4.0 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 5 GB of VRAM.
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No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | — |
Q3_K_S | 3 | 1.5 GB | Low | — |
NVFP4 | 4 | 1.7 GB | Medium | — |
Q4_K_M | 4 | 1.8 GB | Medium | — |
Q5_K_M | 5 | 2.2 GB | High | — |
Q6_K | 6 | 2.5 GB | High | — |
Q8_0 | 8 | 3.2 GB | Very High | — |
F16 | 16 | 6.1 GB | Maximum | — |
Hardware compatibility
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Memory breakdown
Frequently asked questions
stablelm 3b 4e1t (3B parameters) requires approximately 4.0 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc A380 6GB can run stablelm 3b 4e1t with a compatibility score of 52/100. It provides 6 GB of memory and achieves approximately 42.0 tokens per second.
The recommended quantization for stablelm 3b 4e1t is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for stablelm 3b 4e1t: RTX 2060 6GB (score: 53/100), RTX 4050 Laptop 6GB (score: 53/100), GTX 1060 6GB (score: 53/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, stablelm 3b 4e1t is well-suited for chat. It was designed with these use cases in mind.
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