TeichAI
Qwen3 8B DeepSeek v3.2 Speciale Distill (8B parameters) requires approximately 7.6 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 9 GB of VRAM.
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No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | — |
Q3_K_S | 3 | 3.9 GB | Low | — |
NVFP4 | 4 | 4.5 GB | Medium | — |
Q4_K_M | 4 | 4.9 GB | Medium | — |
Q5_K_M | 5 | 5.8 GB | High | — |
Q6_K | 6 | 6.6 GB | High | — |
Q8_0 | 8 | 8.6 GB | Very High | — |
F16 | 16 | 16.4 GB | Maximum | — |
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Frequently asked questions
Qwen3 8B DeepSeek v3.2 Speciale Distill (8B parameters) requires approximately 7.6 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc A580 8GB can run Qwen3 8B DeepSeek v3.2 Speciale Distill with a compatibility score of 52/100. It provides 8 GB of memory and achieves approximately 51.4 tokens per second.
The recommended quantization for Qwen3 8B DeepSeek v3.2 Speciale Distill 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 Qwen3 8B DeepSeek v3.2 Speciale Distill: RTX 3080 10GB (score: 57/100), RTX 2080 Ti 11GB (score: 57/100), RTX 3080 Ti 12GB (score: 57/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Qwen3 8B DeepSeek v3.2 Speciale Distill is well-suited for chat. It was designed with these use cases in mind.
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