Which GPU do you need to run Llama 3.3 70B locally in 2026?
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Damien · LocalIAVRAM by quantization, compatible GPUs, RTX 5090 vs A6000 vs H100, and the cost/performance trade-off against OpenAI APIs.
Llama 3.3 70B is one of the reference open-weight models for local RAG and agents. The catch is simple: 70B only works well if you have enough VRAM and choose the right quantization.
VRAM by quantization
| Q4_K_M | ~47 GB | Acceptable quality, cannot fit on a single 24-32 GB consumer GPU. |
| Q5_K_M | ~58 GB | Very good quality, recommended for RAG. |
| Q8 | ~84 GB | Near-FP16 quality. |
| FP16 | ~168 GB | Reference precision, datacenter-class setup. |
Typical hardware cases
- 24-32 GB VRAM: use smaller models or CPU offload; Llama 70B is not comfortable.
- 48-64 GB VRAM: the 2026 sweet spot, especially two RTX 5090s for Q5.
- 80+ GB VRAM: Q8 and large MoE models become realistic.
Cost and sovereignty
For teams doing hundreds of RAG calls per day, local inference can pay for itself in months while keeping documents inside the organization.
The financial argument becomes stronger when agents call the model repeatedly throughout the day.
Profile recommendation
- Solo developer: one RTX 5090 and a 14B-32B model is usually smarter than forcing 70B.
- Small team: two RTX 5090s for Llama 70B Q5.
- Legal or medical SME: pro GPUs with larger VRAM and a preinstalled RAG stack.
- Custom enterprise: H100, H200, MI300X or multi-rack sizing by quote.
Before buying hardware, test the target model in the LocalIA calculator and check the VRAM margin, not only the GPU name.
Open the calculator / request a quote with your target model, users and constraints.
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