LMSYS
Vicuna 13B (13B parameters) requires approximately 21.9 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 26 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run Vicuna 13B on your machine.
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ollama run vicuna:13bQuick specs
About this model
Related models
Inference speed
Estimated decode speed (tokens/sec) for Vicuna 13B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~151 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? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 151.4 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 96.6 | Offloads |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 87.2 | Offloads |
| RTX 3090 24GB | 24 GB | Q4_K_M | 82.6 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 70.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 58.5 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 55.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 38.2 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 38.2 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 30.3 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 27.7 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 27.1 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 23.3 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 9.5 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 6.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 3.8 | 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 Vicuna 13B (13B) needs at each GGUF quant, and whether it fits a 24 GB card (RTX 4090 / 3090). The recommended Q4_K_M uses ~7.9 GB — about 43% less VRAM than Q8_0, at a small quality cost.
| Quant | Bits | VRAM (weights) | Quality | Fits 24 GB? |
|---|---|---|---|---|
| Q2_K | 2 | 5.1 GB | Low | Tight |
| Q3_K_S | 3 | 6.4 GB | Low | Tight |
| NVFP4 | 4 | 7.3 GB | Medium | Tight |
| Q4_K_Mrecommended | 4 | 7.9 GB | Medium | Offloads |
| Q5_K_M | 5 | 9.4 GB | High | Offloads |
| Q6_K | 6 | 10.7 GB | High | Heavy offload |
| Q8_0 | 8 | 13.9 GB | Very High | Too big |
| F16 | 16 | 26.7 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.
Quality benchmarks
Reasoning
Source: community · 2023-07-29
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
Vicuna 13B (13B parameters) requires approximately 21.9 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 Vicuna 13B with a compatibility score of 68/100. It provides 64 GB of memory and achieves approximately 9.6 tokens per second.
The recommended quantization for Vicuna 13B 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 Vicuna 13B: RTX 5090 32GB (score: 78/100), RTX PRO 4500 Blackwell 32GB (score: 78/100), AMD Instinct MI100 32GB (score: 78/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Vicuna 13B is well-suited for chat as well as instruction. It was designed with these use cases in mind.
See also