Meta AI
70.6 Billion Parameters (Dense GQA)
Meta LLaMA 3.3 70B Instruct VRAM Requirements Guide
Meta's flagship open-weights model matching original 405B benchmarks at 70B efficiency. Features native 128k context with Grouped-Query Attention.
4-Bit (Q4_K_M) - Recommended
44.12 GB
>98% perplexity retention. Standard Ollama/llama.cpp quantization.
8-Bit (Q8_0) - Near Lossless
79.14 GB
Virtually identical to FP16 (~99.9% fidelity). Demands high-capacity VRAM.
16-Bit (FP16 / BF16) - Full
148.50 GB
Uncompressed weights. Requires multi-GPU clusters or datacenter cards.
Recommended Hardware Sweet Spot
Dual NVIDIA RTX 3090 (48GB) or Apple M3 Max (64GB)
Provides sufficient headroom for weights, 8k+ context KV-cache, and CUDA runtime buffers without Out-Of-Memory (OOM) crashes.
Run Meta LLaMA 3.3 70B Instruct Locally
Ollama:
ollama run llama3.3:70b-instruct-q4_K_MvLLM:
vllm serve meta-llama/Llama-3.3-70B-Instruct --tensor-parallel-size 2 --max-model-len 8192 --gpu-memory-utilization 0.95Test Custom Hardware & Context Windows
Simulate your exact GPU, calculate token throughput speeds, or compare cloud rental break-even costs.
Open in GPU Garden Matchmaker →