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.

Architectural Memory Breakdown & KV-Cache Scaling

LLaMA 3.3 70B stacks 80 dense transformer layers with 64 query heads, 8 Grouped-Query Attention (GQA) KV heads, and a 128-element head dimension. At 8,192 tokens in FP16, the KV-cache consumes 2.50 GiB (plus 40.5 GB of `Q4_K_M` weights and driver buffers), fitting neatly inside 48GB dual-GPU rigs. However, scaling to its full 131,072-token window balloons unquantized FP16 KV-cache to 40.0 GiB on its own—pushing total memory past 81 GB unless KV quantization is enabled.

Tier 1 Free Software Fix ($0)

Running Dual RTX 3090 (48GB) and hitting OOM above 16k tokens? Pass `-fa on -ctk q8_0 -ctv q8_0` in llama.cpp or `--kv-cache-dtype fp8` in vLLM to halve KV-cache memory and unlock 32k context with zero hardware spend.

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_M

vLLM:

vllm serve meta-llama/Llama-3.3-70B-Instruct --tensor-parallel-size 2 --max-model-len 8192 --gpu-memory-utilization 0.95

Frequently Asked Questions

Can a single 24GB RTX 4090 or 32GB RTX 5090 run LLaMA 3.3 70B?

At Q4_K_M (44.12 GB), neither single GPU can hold all 80 layers in VRAM. Offloading excess layers over PCIe to DDR5 RAM drops generation speed below 2.5 tok/s; use Dual RTX 3090 (48GB), Mac 64GB+, or an 80GB A100 cloud pod.

How much VRAM does LLaMA 3.3 70B need at 128k context?

With Q4_K_M weights plus FP8 quantized KV-cache (20.0 GiB), 128k single-stream inference requires ~62.5 GB VRAM, making Dual RTX 5090 (64GB), RTX PRO 6000 (96GB), or a single 80GB H100/A100 ideal.

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