Hugging-quants
Llama 3.2 1B Instruct Q8 0
Limited data available — some specs may be incomplete or estimated.
740.5KDownloads44Likes0K tokensContextUnknownLicense5 EntryQuality
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
Parameters1B
Architecturedense
Context0K tokens
Modalitytext
Min RAM0.4 GB
Rec. RAM0.8 GB (Q6_K)
LicenseUnknown
FamilyLlama
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Quantization options
VRAM estimates by quant level
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.4 GB | Low | — |
Q3_K_S | 3 | 0.5 GB | Low | — |
NVFP4 | 4 | 0.6 GB | Medium | — |
Q4_K_M | 4 | 0.6 GB | Medium | — |
Q5_K_M | 5 | 0.7 GB | High | — |
Q6_K | 6 | 0.8 GB | High | — |
Q8_0 | 8 | 1.1 GB | Very High | — |
F16 | 16 | 2.1 GB | Maximum | — |
Hardware compatibility
Fit estimates across all hardware
Computing compatibility...
Memory breakdown
Reference: RTX 2060 6GB
Weights0.8 GB
KV Cache0.1 GB
Runtime1.2 GB
Headroom0.6 GB
Frequently asked questions
FAQ — Llama 3.2 1B Instruct Q8 0
See also