Hugging-quants
Llama 3.2 1B Instruct Q8 0 (1B parameters) requires approximately 2.7 GB of VRAM with Q6_K quantization. For the best balance of quality and speed, we recommend hardware with at least 4 GB of VRAM.
Get started
— copy & paste to run locallyCopy-paste commands to run Llama 3.2 1B Instruct Q8 0 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF" \
--hf-file "Llama-3.2-1B-Instruct-Q8_0-GGUF-Q6_K.gguf" \
-c 4096 -ngl 99Quick specs
Related models
Inference speed
Estimated decode speed (tokens/sec) for Llama 3.2 1B Instruct Q8 0 at Q6_K across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~19 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 | Q6_K | 19.0 | Fits |
| RTX 4090 24GB | 24 GB | Q6_K | 16.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q6_K | 16.0 | Fits |
| RTX 3090 24GB | 24 GB | Q6_K | 14.0 | Fits |
| RTX 4070 12GB | 12 GB | Q6_K | 14.0 | Fits |
| RTX 3060 12GB | 12 GB | Q6_K | 14.0 | Fits |
| RTX 4060 8GB | 8 GB | Q6_K | 14.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q6_K | 14.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q6_K | 14.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q6_K | 14.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q6_K | 14.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q6_K | 14.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q6_K | 14.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q6_K | 14.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q6_K | 14.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q6_K | 14.0 | Fits |
Estimates for single-stream decoding at Q6_K; 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 Llama 3.2 1B Instruct Q8 0 (1B) needs at each GGUF quant, and whether it fits a 24 GB card (RTX 4090 / 3090). The recommended Q6_K uses ~0.8 GB — about 27% less VRAM than Q8_0, at a small quality cost.
| Quant | Bits | VRAM (weights) | Quality | Fits 24 GB? |
|---|---|---|---|---|
| Q2_K | 2 | 0.4 GB | Low | Fits |
| Q3_K_S | 3 | 0.5 GB | Low | Fits |
| NVFP4 | 4 | 0.6 GB | Medium | Fits |
| Q4_K_M | 4 | 0.6 GB | Medium | Fits |
| Q5_K_M | 5 | 0.7 GB | High | Fits |
| Q6_Krecommended | 6 | 0.8 GB | High | Fits |
| Q8_0 | 8 | 1.1 GB | Very High | Fits |
| F16 | 16 | 2.1 GB | Maximum | Fits |
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
Llama 3.2 1B Instruct Q8 0 (1B parameters) requires approximately 2.7 GB of VRAM with Q6_K quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc A380 6GB can run Llama 3.2 1B Instruct Q8 0 with a compatibility score of 45/100. It provides 6 GB of memory and achieves approximately 14.0 tokens per second.
The recommended quantization for Llama 3.2 1B Instruct Q8 0 is Q6_K, 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 Llama 3.2 1B Instruct Q8 0: GTX 1650 4GB (score: 50/100), RTX 3050 Ti Laptop 4GB (score: 50/100), Intel Arc A370M 4GB (score: 48/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Llama 3.2 1B Instruct Q8 0 is well-suited for chat. It was designed with these use cases in mind.
See also
