Can I Run Meta Llama 3.1 405B Instruct on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Meta Llama 3.1 405B Instruct (405B) on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) (48 GB).
✕ Out of Memory — Exceeds VRAM Capacity
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run llama3.1:405bLocal Hardware Configurations & Pricing
⚠️ Out of VRAM: High-Capacity Hardware UpgradesNVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization
2x 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
⚠️ 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 3.1 405B Instruct @ 260.72 GB VRAM)
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 810 GB | 8.46 GB | 847.56 GB (1766%) | 0 t/s | OOM |
| Q8_08.5 bpw | 429.3 GB | 8.46 GB | 453.54 GB (945%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 332.1 GB | 8.46 GB | 352.93 GB (735%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 287.55 GB | 8.46 GB | 306.82 GB (639%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 243 GB | 8.46 GB | 260.72 GB (543%) | 0 t/s | OOM |
| FP44.25 bpw | 214.65 GB | 8.46 GB | 231.37 GB (482%) | 0 t/s | OOM |
| IQ3_XS3.3 bpw | 166.05 GB | 8.46 GB | 181.07 GB (377%) | 0 t/s | OOM |
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Frequently Asked Questions: Meta Llama 3.1 405B Instruct on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
How much VRAM does Meta Llama 3.1 405B Instruct need on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Meta Llama 3.1 405B Instruct requires 260.72 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Meta Llama 3.1 405B Instruct achieve on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)?
Based on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)'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.