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Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored (48B parameters) requires approximately 36.7 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 43 GB of VRAM.
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Inference speed
Estimated decode speed (tokens/sec) for Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~40 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 40.1 | Tight |
| 48 GB | Q4_K_M | 21.1 | Tight | |
| 32 GB | Q4_K_M | 20.6 | Too big | |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 20.5 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 20.5 | Tight |
| 48 GB | Q4_K_M | 19.4 | Tight | |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 19.0 | Fits |
| 48 GB | Q4_K_M | 17.1 | Tight | |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 15.8 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 15.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 10.0 | Heavy offload |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 8.2 | Tight |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 7.5 | Tight |
| 24 GB | Q4_K_M | 7.3 | Too big | |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 6.7 | Too big |
| 24 GB | Q4_K_M | 6.2 | Too big | |
| 16 GB | Q4_K_M | 3.1 | Too big | |
| 12 GB | Q4_K_M | 2.0 | Too big | |
| 12 GB | Q4_K_M | 2.0 | Too big | |
| 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
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Quantization
How much VRAM Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored (48B) needs at each GGUF quant, and whether it fits a 24 GB card (RTX 4090 / 3090). The recommended Q4_K_M uses ~29.3 GB — about 43% less VRAM than Q8_0, at a small quality cost.
| Quant | Bits | VRAM (weights) | Quality | Fits 24 GB? |
|---|---|---|---|---|
| Q2_K | 2 | 18.7 GB | Low | Heavy offload |
| Q3_K_S | 3 | 23.5 GB | Low | Too big |
| NVFP4 | 4 | 26.9 GB | Medium | Too big |
| Q4_K_Mrecommended | 4 | 29.3 GB | Medium | Too big |
| Q5_K_M | 5 | 34.6 GB | High | Too big |
| Q6_K | 6 | 39.4 GB | High | Too big |
| Q8_0 | 8 | 51.4 GB | Very High | Too big |
| F16 | 16 | 98.4 GB | Maximum | Too big |
VRAM shown is quantized weights only; add ~1–3 GB runtime overhead plus KV cache for your context length. Lower quants trade quality for memory — Q4_K_M is the usual sweet spot; Q2/Q3 only when you must fit a bigger model.
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored (48B parameters) requires approximately 36.7 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Mac mini M4 64GB can run Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored with a compatibility score of 45/100. It provides 64 GB of memory and achieves approximately 5.3 tokens per second.
The recommended quantization for Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored 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 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored: NVIDIA H100 80GB (score: 54/100), NVIDIA H800 80GB (score: 53/100), AMD Instinct MI210 64GB (score: 53/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Qwen3 48B A4B Savant Commander Distill 12X Closed Open Heretic Uncensored is well-suited for chat as well as reasoning. It was designed with these use cases in mind.
See also