Can I Run Meta Llama 3.3 70B Instruct on NVIDIA GeForce RTX 5070 (12GB GDDR7)?
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 5070 (12GB GDDR7) (12 GB).
✕ Out of Memory — Exceeds VRAM Capacity
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run llama3.3:70bLocal 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 70B Q4_K_M & Qwen 3.8 27B Q8
Apple Mac Studio M4 Max (128GB Unified Memory)
Running Llama 4 Scout 109B & 70B Q8 locally with 128K+ context
NVIDIA GeForce RTX 5070 (12GB GDDR7)
High-efficiency 12GB entry point with FP4 tensor acceleration
⚠️ 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 (1269%) | 0 t/s | OOM |
| Q8_08.5 bpw | 74.84 GB | 5.37 GB | 83.58 GB (697%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 57.89 GB | 5.37 GB | 66.04 GB (550%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 50.13 GB | 5.37 GB | 58 GB (483%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 42.36 GB | 5.37 GB | 49.96 GB (416%) | 0 t/s | OOM |
| FP44.25 bpw | 37.42 GB | 5.37 GB | 44.85 GB (374%) | 0 t/s | OOM |
| IQ3_XS3.3 bpw | 28.95 GB | 5.37 GB | 36.08 GB (301%) | 0 t/s | OOM |
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Frequently Asked Questions: Meta Llama 3.3 70B Instruct on NVIDIA GeForce RTX 5070 (12GB GDDR7)
How much VRAM does Meta Llama 3.3 70B Instruct need on NVIDIA GeForce RTX 5070 (12GB GDDR7)?
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 5070 (12GB GDDR7)?
Based on NVIDIA GeForce RTX 5070 (12GB GDDR7)'s 672 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.