Run hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF locally
hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF is a compact instruction-tuned chat model with 1.24 billion parameters, built on the llama architecture. It has been downloaded 601,437 times.
To run hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF locally at a 4,096-token context, its quantized versions need between 2.16 GB (Q8_0, lowest quality) and 2.16 GB (Q8_0, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q8_0, needing about 2.16 GB. That means hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q8_0 | 8.55 | Excellent | 1.23 GB | 0.12 GB | 2.16 GB | 325.1 t/s | Fits in VRAM |
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 hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF?
You need about 2.16 GB of VRAM to run hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF entirely on the GPU using the Q8_0 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF fully on the GPU using Q8_0 (about 2.16 GB).
Can I run hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF fully on the GPU using Q8_0 (about 2.16 GB).
Can I run hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF fully on the GPU using Q8_0 (about 2.16 GB).
What is the best quantization for hugging-quants/Llama-3.2-1B-Instruct-Q8_0-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.