Meta Llama 3.1 8B Instruct (Q4_0_4_4)on CPU Only
Overview
Meta Llama 3.1 8B Instruct is a 8B parameter dense language model by Meta, with code, multilingual, tool-calls capabilities. It supports a context window of up to 131,072 tokens.
Meta Llama 3.1 8B Instruct is an 8-billion-parameter dense transformer model from Meta, designed for instruction following, code generation, and multilingual tasks. It offers a strong balance of quality and efficiency in the small-model category, outperforming many 7B-class alternatives on standard benchmarks. The model supports tool calling and eight languages including English, German, and French. With a 128K context window and flash attention support, it runs comfortably on a single consumer GPU at Q4 quantization levels.
At Q4_0_4_4 quantization (low quality tier), the model weighs 4.34 GB. This exceeds the 0 GB of VRAM on CPU Only. Inference is still possible via CPU offload or memory-mapped loading from disk, but expect significantly reduced performance.
A CPU-only configuration with no GPU acceleration. Inference runs entirely on the CPU, which is significantly slower than GPU-accelerated setups but requires no special hardware. Performance and maximum model size depend on available system RAM. Suitable for testing, development, or deployments where no GPU is available.
Hardware Requirements
| Model size | 4.34 GB |
| VRAM available | 0 GB |
| VRAM used | 0 GB |
| System RAM | |
| Min RAM required | 4.3 GB |
| GPU layers | 0 / 32 |
| Context size | 131,072 |
| Backend | cpu |
| Flash attention | No |
| Reading from disk | Yes |
Performance Notes
Deploy
Command
helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/meta-llama-3-1-8b-instruct/q4_0_4_4/cpu.yaml) applyGenerated values.yaml
/values/meta-llama-3-1-8b-instruct/q4_0_4_4/cpu.yaml
Loading values…Frequently Asked Questions
How much VRAM does Meta Llama 3.1 8B Instruct (Q4_0_4_4) need?
The Q4_0_4_4 quantization of Meta Llama 3.1 8B Instruct requires 4.34 GB. The 0 GB of VRAM on CPU Only is insufficient for GPU layers, so inference runs on CPU.
Can I run Meta Llama 3.1 8B Instruct on CPU Only?
It is possible but not recommended. CPU Only does not have enough VRAM to accelerate Meta Llama 3.1 8B Instruct (Q4_0_4_4), so inference will rely on CPU and system RAM.
What is quantization?
Quantization reduces a model's numerical precision from its original floating-point format to a more compact representation. This shrinks the file size and VRAM footprint, making it possible to run large models on consumer hardware. The trade-off is a small reduction in output quality. Q4_0_4_4 compresses Meta Llama 3.1 8B Instruct from its original size down to 4.34 GB.
What quantization should I choose for Meta Llama 3.1 8B Instruct?
Q4_0_4_4 is a low-quality quantization. Higher-quality quants (Q8, Q6) preserve more model accuracy but need more VRAM. Lower quants (Q4, Q3, Q2) reduce VRAM usage at the cost of some quality. Choose based on your available hardware and quality requirements.
Why are some layers offloaded to CPU?
CPU Only has 0 GB of VRAM, but Meta Llama 3.1 8B Instruct (Q4_0_4_4) requires approximately 4.34 GB. Only 0 of 32 layers fit in VRAM; the remaining layers run on CPU, which is slower but still functional.
How do I run Meta Llama 3.1 8B Instruct (Q4_0_4_4) with Ollama?
Run ollama run llama3.1:8b-instruct-q4_0_4_4 to start Meta Llama 3.1 8B Instruct (Q4_0_4_4). Ollama handles downloading the model weights automatically on first run.