Run bartowski/Llama-3.2-1B-Instruct-GGUF locally
Llama-3.2-1B-Instruct is a 1.24 billion parameter AI model designed for instruction-following tasks. It belongs to the Llama family of open-source models and is licensed under the llama3.2 terms. The model is optimized for deployment in applications requiring structured responses and general-purpose language understanding.
To run bartowski/Llama-3.2-1B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 1.54 GB (IQ3_M, lowest quality) and 3.23 GB (F16, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is F16, needing about 3.23 GB. That means bartowski/Llama-3.2-1B-Instruct-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 |
|---|---|---|---|---|---|---|---|
| IQ3_M | 4.25 | Good | 0.61 GB | 0.12 GB | 1.54 GB | 653.4 t/s | Fits in VRAM |
| Q3_K_L | 4.74 | Good | 0.68 GB | 0.12 GB | 1.61 GB | 586.3 t/s | Fits in VRAM |
| IQ4_XS | 4.81 | Good | 0.69 GB | 0.12 GB | 1.62 GB | 577.9 t/s | Fits in VRAM |
| Q4_0_4_4 | 4.99 | Good | 0.72 GB | 0.12 GB | 1.64 GB | 557.1 t/s | Fits in VRAM |
| Q4_0_4_8 | 4.99 | Good | 0.72 GB | 0.12 GB | 1.64 GB | 557.1 t/s | Fits in VRAM |
| Q4_0_8_8 | 4.99 | Good | 0.72 GB | 0.12 GB | 1.64 GB | 557.1 t/s | Fits in VRAM |
| Q4_0 | 5.0 | Very good | 0.72 GB | 0.12 GB | 1.64 GB | 555.6 t/s | Fits in VRAM |
| Q4_K_S | 5.02 | Very good | 0.72 GB | 0.12 GB | 1.65 GB | 553.7 t/s | Fits in VRAM |
| Q3_K_XL | 5.15 | Very good | 0.74 GB | 0.12 GB | 1.67 GB | 539.5 t/s | Fits in VRAM |
| Q4_K_M | 5.23 | Very good | 0.75 GB | 0.12 GB | 1.68 GB | 531.8 t/s | Fits in VRAM |
| Q4_K_L | 5.64 | Very good | 0.81 GB | 0.12 GB | 1.74 GB | 492.9 t/s | Fits in VRAM |
| Q5_K_S | 5.78 | Very good | 0.83 GB | 0.12 GB | 1.76 GB | 481.2 t/s | Fits in VRAM |
| Q5_K_M | 5.9 | Very good | 0.85 GB | 0.12 GB | 1.77 GB | 471.2 t/s | Fits in VRAM |
| Q5_K_L | 6.31 | Very good | 0.91 GB | 0.12 GB | 1.83 GB | 440.5 t/s | Fits in VRAM |
| Q6_K | 6.61 | Excellent | 0.95 GB | 0.12 GB | 1.88 GB | 420.3 t/s | Fits in VRAM |
| Q6_K_L | 7.03 | Excellent | 1.01 GB | 0.12 GB | 1.94 GB | 395.7 t/s | Fits in VRAM |
| Q8_0 | 8.55 | Excellent | 1.23 GB | 0.12 GB | 2.16 GB | 325.1 t/s | Fits in VRAM |
| F16 | 16.05 | Excellent | 2.31 GB | 0.12 GB | 3.23 GB | 173.2 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 bartowski/Llama-3.2-1B-Instruct-GGUF?
You need about 3.23 GB of VRAM to run bartowski/Llama-3.2-1B-Instruct-GGUF entirely on the GPU using the F16 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run bartowski/Llama-3.2-1B-Instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run bartowski/Llama-3.2-1B-Instruct-GGUF fully on the GPU using F16 (about 3.23 GB).
Can I run bartowski/Llama-3.2-1B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run bartowski/Llama-3.2-1B-Instruct-GGUF fully on the GPU using F16 (about 3.23 GB).
Can I run bartowski/Llama-3.2-1B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run bartowski/Llama-3.2-1B-Instruct-GGUF fully on the GPU using F16 (about 3.23 GB).
What is the best quantization for bartowski/Llama-3.2-1B-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.