Llama 3.3 70B Instruct (Q4_K_M)on CPU Only
Overview
Llama 3.3 70B Instruct is a 70B parameter dense language model by Meta, with code, multilingual, tool-calls capabilities. It supports a context window of up to 131,072 tokens.
Llama 3.3 70B Instruct is a 70-billion-parameter dense transformer model from Meta, optimized for instruction following, code generation, and multilingual conversation. It delivers performance competitive with larger models in the Llama family while remaining practical for single-node GPU deployments. The model supports tool calling and eight languages including English, French, Spanish, and German. With a 128K context window and grouped-query attention, it quantizes efficiently down to Q4 levels for self-hosted inference on consumer hardware.
At Q4_K_M quantization (medium quality tier), the model weighs 39.6 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 | 39.6 GB |
| VRAM available | 0 GB |
| VRAM used | 0 GB |
| System RAM | |
| Min RAM required | 39.6 GB |
| GPU layers | 0 / 80 |
| 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/llama-3-3-70b-instruct/q4_k_m/cpu.yaml) applyGenerated values.yaml
/values/llama-3-3-70b-instruct/q4_k_m/cpu.yaml
Loading values…Frequently Asked Questions
How much VRAM does Llama 3.3 70B Instruct (Q4_K_M) need?
The Q4_K_M quantization of Llama 3.3 70B Instruct requires 39.6 GB. The 0 GB of VRAM on CPU Only is insufficient for GPU layers, so inference runs on CPU.
Can I run Llama 3.3 70B Instruct on CPU Only?
It is possible but not recommended. CPU Only does not have enough VRAM to accelerate Llama 3.3 70B Instruct (Q4_K_M), 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_K_M compresses Llama 3.3 70B Instruct from its original size down to 39.6 GB.
What quantization should I choose for Llama 3.3 70B Instruct?
Q4_K_M is a medium-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 Llama 3.3 70B Instruct (Q4_K_M) requires approximately 39.6 GB. Only 0 of 80 layers fit in VRAM; the remaining layers run on CPU, which is slower but still functional.
How do I run Llama 3.3 70B Instruct (Q4_K_M) with Ollama?
Run ollama run llama3.3:70b-instruct-q4_k_m to start Llama 3.3 70B Instruct (Q4_K_M). Ollama handles downloading the model weights automatically on first run.