solar-pro-preview-instruct GGUF size and VRAM requirements
MaziyarPanahi/solar-pro-preview-instruct-GGUF is a large instruction-tuned chat model with 22.14 billion parameters, built on the llama architecture. It has been downloaded 101,110 times.
To run MaziyarPanahi/solar-pro-preview-instruct-GGUF locally at a 4,096-token context, its quantized versions need between 6.51 GB (IQ1_S, lowest quality) and 43.29 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is IQ1_M, needing about 6.92 GB. That means MaziyarPanahi/solar-pro-preview-instruct-GGUF fits entirely in the VRAM of an 8 GB GPU or larger, running fully on the GPU.
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| IQ1_S | 1.73 | Very low | 4.46 GB | 1.25 GB | 6.51 GB | 89.7 t/s | Fits in VRAM |
| IQ1_M | 1.89 | Very low | 4.87 GB | 1.25 GB | 6.92 GB | 82.1 t/s | Fits in VRAM |
| IQ2_XS | 2.39 | Very low | 6.16 GB | 1.25 GB | 8.21 GB | 8.1 t/s | Offload |
| Q2_K | 2.97 | Low | 7.65 GB | 1.25 GB | 9.7 GB | 6.5 t/s | Offload |
| IQ3_XS | 3.3 | Low | 8.5 GB | 1.25 GB | 10.55 GB | 5.9 t/s | Offload |
| Q3_K_S | 3.46 | Fair | 8.92 GB | 1.25 GB | 10.97 GB | 5.6 t/s | Offload |
| Q3_K_M | 3.86 | Fair | 9.95 GB | 1.25 GB | 12.0 GB | 5.0 t/s | Offload |
| Q3_K_L | 4.2 | Good | 10.84 GB | 1.25 GB | 12.89 GB | 4.6 t/s | Offload |
| IQ4_XS | 4.29 | Good | 11.06 GB | 1.25 GB | 13.11 GB | 4.5 t/s | Offload |
| Q4_K_S | 4.55 | Good | 11.73 GB | 1.25 GB | 13.78 GB | 4.3 t/s | Offload |
| Q4_K_M | 4.81 | Good | 12.4 GB | 1.25 GB | 14.45 GB | 4.0 t/s | Offload |
| Q5_K_S | 5.51 | Very good | 14.2 GB | 1.25 GB | 16.25 GB | 3.5 t/s | Offload |
| Q5_K_M | 5.66 | Very good | 14.59 GB | 1.25 GB | 16.64 GB | 3.4 t/s | Offload |
| Q6_K | 6.56 | Excellent | 16.92 GB | 1.25 GB | 18.97 GB | 3.0 t/s | Offload |
| Q8_0 | 8.5 | Excellent | 21.91 GB | 1.25 GB | 23.96 GB | 2.3 t/s | Offload |
| GGUF | 16.0 | Excellent | 41.24 GB | 1.25 GB | 43.29 GB | — | Insufficient |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run MaziyarPanahi/solar-pro-preview-instruct-GGUF?
You need about 6.92 GB of VRAM to run MaziyarPanahi/solar-pro-preview-instruct-GGUF entirely on the GPU using the IQ1_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/solar-pro-preview-instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run MaziyarPanahi/solar-pro-preview-instruct-GGUF fully on the GPU using IQ1_M (about 6.92 GB).
Can I run MaziyarPanahi/solar-pro-preview-instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/solar-pro-preview-instruct-GGUF fully on the GPU using Q4_K_M (about 14.45 GB).
Can I run MaziyarPanahi/solar-pro-preview-instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/solar-pro-preview-instruct-GGUF fully on the GPU using Q8_0 (about 23.96 GB).
What is the best quantization for MaziyarPanahi/solar-pro-preview-instruct-GGUF?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.