RichardErkhov
stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 52.7 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 61 GB of VRAM.
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No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | — |
Q3_K_S | 3 | 34.3 GB | Low | — |
NVFP4 | 4 | 39.2 GB | Medium | — |
Q4_K_M | 4 | 42.7 GB | Medium | — |
Q5_K_M | 5 | 50.4 GB | High | — |
Q6_K | 6 | 57.4 GB | High | — |
Q8_0 | 8 | 74.9 GB | Very High | — |
F16 | 16 | 143.5 GB | Maximum | — |
Hardware compatibility
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Memory breakdown
Frequently asked questions
stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 52.7 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, MacBook Pro M3 Max 128GB can run stabilityai japanese stablelm instruct beta 70b with a compatibility score of 48/100. It provides 128 GB of memory and achieves approximately 5.6 tokens per second.
The recommended quantization for stabilityai japanese stablelm instruct beta 70b 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 stabilityai japanese stablelm instruct beta 70b: NVIDIA H100 80GB (score: 55/100), NVIDIA H800 80GB (score: 55/100), NVIDIA GH200 96GB (score: 55/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, stabilityai japanese stablelm instruct beta 70b is well-suited for chat. It was designed with these use cases in mind.
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