Can I Run Meta Llama 3.3 70B Instruct on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Meta Llama 3.3 70B Instruct (70.6B) on NVIDIA GeForce RTX 3090 (24GB GDDR6X) (24 GB).
✕ Out of Memory — Exceeds VRAM Capacity
Local Hardware Configurations & Pricing
Out of VRAM: High-Capacity Hardware Upgrades2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0
Apple Mac Mini M4 Pro (64GB Unified Memory)
Silent 65W desktop running 32B–70B models with large KV context
Apple Mac Studio M4 Max (128GB Unified Memory)
Running Mistral Large 2 123B & Llama 3.3 70B Q8_0 locally with 128K context
NVIDIA GeForce RTX 3090 (24GB GDDR6X)
The #1 budget 24GB card for local LLMs, running 14B–32B models and Q4 70B quantized
Out of VRAM? Don't downgrade model quality.
Out of VRAMSpin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (Meta Llama 3.3 70B Instruct @ 49.96 GB VRAM)
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 141.2 GB | 5.37 GB | 152.26 GB (634%) | 0 t/s | OOM |
| Q8_08.5 bpw | 74.84 GB | 5.37 GB | 83.58 GB (348%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 57.89 GB | 5.37 GB | 66.04 GB (275%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 50.13 GB | 5.37 GB | 58 GB (242%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 42.36 GB | 5.37 GB | 49.96 GB (208%) | 0 t/s | OOM |
| FP44.25 bpw | 37.42 GB | 5.37 GB | 44.85 GB (187%) | 0 t/s | OOM |
| IQ3_XS3.3 bpw | 28.95 GB | 5.37 GB | 36.08 GB (150%) | 0 t/s | OOM |
Frequently Asked Questions: Meta Llama 3.3 70B Instruct on NVIDIA GeForce RTX 3090 (24GB GDDR6X)
How much VRAM does Meta Llama 3.3 70B Instruct need on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Meta Llama 3.3 70B Instruct requires 49.96 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Meta Llama 3.3 70B Instruct achieve on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?
Based on NVIDIA GeForce RTX 3090 (24GB GDDR6X)'s 936 GB/s memory bandwidth, expected decode speed is approximately 0 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.