Llama vs Qwen vs DeepSeek: What Hardware Do You Need to Host Them?
Running your own AI model is exciting, but the first thing to figure out is whether your hardware can handle it. Here is a simple summary of what the three most popular open-weight model families demand.
What fills up your GPU memory?
Two things use your VRAM.
Model weights: parameter count multiplied by precision. A model uses 2 bytes per parameter at FP16, 1 byte at 8-bit and 0.5 bytes at 4-bit.
KV cache: this grows with context length and with the number of users chatting at the same time. Llama 3.3 70B needs about 2.6GB of cache for one 8,000-token conversation, so ten users add roughly 26GB on top of the weights.
Quick comparison
Llama 3.1 8B (8-bit): 16-24GB of VRAM. One RTX 4090 or RTX 4500 Ada is enough.
Llama 3.3 70B (8-bit): about 97GB with KV cache. Use 2x A100 80GB or 1x H200.
DeepSeek V3/R1 (FP8): about 746GB. Use an 8x H200 or B200 server node.
R1-Distill-Llama-70B: 40-48GB. One RTX 6000 Ada (48GB) can run it.
Qwen3.8-Max (FP8): about 2,500GB or more. It needs a multi-node GPU cluster.
Key takeaways
Dense models like Llama scale in a predictable way, which makes them easy to size.
MoE models like DeepSeek need huge memory but less compute per token.
Distilled models give the best value for most teams.
NVMe storage, system RAM (1.5x to 2x your total VRAM), PCIe lanes and cooling decide how well the build performs.
Read the complete guide with all the details and server recommendations:
https://www.leoservers.com/blogs/qwen-vs-deepseek-vs-llama-hardware-requirements/
El dato del KV cache me voló la cabeza: 2.6GB por conversación de 8k tokens y con 10 usuarios ya son 26GB extra solo de contexto. Yo probé R1-Distill-Llama-70B en una RTX 6000 Ada y confirma los 40-48GB, la diferencia se nota cuando sumás usuarios. La regla de system RAM a 1.5x-2x del VRAM total es súper práctico para no quedarse corto.