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 / ask us for advice with your target model, users and constraints.
Frequently asked questions
What is the minimum GPU to run Llama 3.3 70B locally?+
An RTX 5090 (32 GB) in Q3_K_M works with quality trade-offs. For comfortable Q5_K_M: 2x RTX 5090 (64 GB) or 1x A6000 (48 GB). For Q8 production: 2x A6000 NVLink (96 GB) or 1x H100 80 GB.
How much VRAM for Llama 70B in Q4?+
Around 44 GB in Q4_K_M (params x 0.5625 bytes + 20% overhead for KV cache and context). So 48 GB minimum (A6000) or 64 GB (2x 5090). The 32 GB of a single 5090 is not enough for Q4 without offload.
What differences between Q3, Q4, Q5 and Q8 on Llama 70B?+
Q3: 32 GB VRAM, degraded reasoning quality. Q4: 44 GB, the consumer sweet spot. Q5: 52 GB, indistinguishable from FP16 in chat. Q8: 78 GB, near-FP16 for production. For most uses, Q5_K_M is the best quality/VRAM trade-off.
RTX 4090 or RTX 5090 for Llama 70B in 2026?+
The RTX 5090 (32 GB) wins on VRAM capacity (+33%), bandwidth (~1,800 GB/s vs 1,000) and FP4 support. In mid-2026 both sit at similar prices due to the AI shortage, so prefer the 5090 to be future-proof.
Is a Pro build (~EUR 11,990) well sized for Llama 70B?+
Yes, it is the 2026 sweet spot. 2x RTX 5090 = 64 GB total VRAM via vLLM tensor parallelism. Llama 3.3 70B Q5_K_M runs at 28-35 tok/s single-user and reaches 90-100 tok/s combined in batching for 5 users.
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