Llama · Llama 3.1 · 405.9B parameters
Llama 3.1 405B VRAM requirements
Llama 3.1 405B has 126 layers and uses grouped-query attention (8 KV heads). At Q4_K_M the weights come to 229.5 GB.
Won't fit
234.4 GB of 21.8 GB · 1077%Short by 212.7 GB. You can run it with 9 of 126 layers on the RTX 4090 and the rest in system RAM, at roughly 0.18 tok/s — usable for batch work, painful for chat. A smaller quantisation or a shorter context is usually the better trade.
Every quantisation of Llama 3.1 405B on a RTX 4090
Highlighted row is the highest quality that still fits at 8K context.
| Quantisation | bpw | Weights | Total | Verdict | Max ctx | tok/s |
|---|---|---|---|---|---|---|
| FP16 / BF16 | 16.00 | 756.0 GB | 760.9 GB | Won't fit | — | 0.05 |
| INT8 / W8A8 | 8.50 | 400.7 GB | 405.6 GB | Won't fit | — | 0.10 |
| Q8_0 (GGUF) | 8.50 | 400.7 GB | 405.6 GB | Won't fit | — | 0.10 |
| FP8 (E4M3) | 8.00 | 378.0 GB | 382.9 GB | Won't fit | — | 0.10 |
| Q6_K | 6.56 | 309.9 GB | 314.9 GB | Won't fit | — | 0.13 |
| Q5_K_M | 5.67 | 267.9 GB | 272.9 GB | Won't fit | — | 0.15 |
| Q5_K_S | 5.52 | 260.8 GB | 265.8 GB | Won't fit | — | 0.15 |
| Q4_K_M | 4.85 | 229.5 GB | 234.4 GB | Won't fit | — | 0.18 |
| Q4_K_S | 4.58 | 216.8 GB | 221.8 GB | Won't fit | — | 0.19 |
| Q4_0 | 4.55 | 215.4 GB | 220.4 GB | Won't fit | — | 0.19 |
| AWQ 4-bit | 4.25 | 206.5 GB | 211.5 GB | Won't fit | — | 0.20 |
| GPTQ 4-bit | 4.25 | 206.5 GB | 211.5 GB | Won't fit | — | 0.20 |
| MXFP4 | 4.25 | 206.5 GB | 211.5 GB | Won't fit | — | 0.20 |
| IQ4_XS | 4.25 | 201.4 GB | 206.4 GB | Won't fit | — | 0.20 |
| Q3_K_M | 3.91 | 185.5 GB | 190.5 GB | Won't fit | — | 0.22 |
| IQ3_M | 3.70 | 175.7 GB | 180.7 GB | Won't fit | — | 0.24 |
| IQ3_XXS | 3.06 | 145.8 GB | 150.7 GB | Won't fit | — | 0.29 |
| Q2_K | 2.63 | 125.7 GB | 130.6 GB | Won't fit | — | 0.34 |
| IQ2_XXS | 2.06 | 99.0 GB | 104.0 GB | Won't fit | — | 0.45 |
| IQ1_M | 1.75 | 84.5 GB | 89.5 GB | Won't fit | — | 0.54 |
Llama 3.1 405B on each GPU
Q4_K_M weights at 8K context, single card, monitor attached.
| GPU | VRAM | GB/s | Verdict | Max ctx | tok/s |
|---|---|---|---|---|---|
| Mac Studio M3 Ultra 256GB | 256 | 819 | Won't fit | — | 0.63 |
| H100 SXM 80GB | 80 | 3350 | Won't fit | — | 0.27 |
| Mac Studio M4 Max 128GB | 128 | 546 | Won't fit | — | 0.27 |
| NVIDIA DGX Spark (GB10) | 128 | 273 | Won't fit | — | 0.26 |
| A100 80GB | 80 | 2039 | Won't fit | — | 0.24 |
| Ryzen AI Max+ 395 128GB | 128 | 256 | Won't fit | — | 0.21 |
| RTX A6000 | 48 | 768 | Won't fit | — | 0.20 |
| RTX 5090 | 32 | 1792 | Won't fit | — | 0.20 |
| Mac Mini M4 Pro 48GB | 48 | 273 | Won't fit | — | 0.20 |
| L40S | 48 | 864 | Won't fit | — | 0.20 |
| RTX 5080 | 16 | 960 | Won't fit | — | 0.19 |
| RTX 5070 Ti | 16 | 896 | Won't fit | — | 0.19 |
| RTX 5060 Ti 16GB | 16 | 448 | Won't fit | — | 0.19 |
| RTX 5070 | 12 | 672 | Won't fit | — | 0.19 |
| RTX 3090 | 24 | 936 | Won't fit | — | 0.18 |
| RTX 4090 | 24 | 1008 | Won't fit | — | 0.18 |
| RTX 3060 12GB | 12 | 360 | Won't fit | — | 0.18 |
| RTX 3080 10GB | 10 | 760 | Won't fit | — | 0.17 |
| RTX 4080 Super | 16 | 736 | Won't fit | — | 0.17 |
| RTX 4070 Ti Super | 16 | 672 | Won't fit | — | 0.17 |
| RTX 4060 Ti 16GB | 16 | 288 | Won't fit | — | 0.17 |
| RTX 4070 Super | 12 | 504 | Won't fit | — | 0.17 |
| RTX 4070 | 12 | 504 | Won't fit | — | 0.17 |
| Radeon RX 7900 XTX | 24 | 960 | Won't fit | — | 0.17 |
| Arc B580 | 12 | 456 | Won't fit | — | 0.14 |
Architecture
| Parameters | 405.9B |
| Layers | 126 |
| Hidden size | 16384 |
| Attention heads / KV heads | 128 / 8 |
| Head dimension | 128 |
| Vocabulary | 128,256 |
| Trained context | 128K |
| KV cache per 1K tokens | 0 GB |
| Hugging Face | meta-llama/Llama-3.1-405B-Instruct |
The Llama family
Meta's open-weight series, and the default target for most local tooling. Every Llama 3.x model uses grouped-query attention with 8 KV heads, so the cache stays modest even at 70B. Llama 4 moved to mixture-of-experts: Scout and Maverick occupy 109B and 400B of memory but read only 17B per token.