{"id":1284,"date":"2026-08-14T09:23:23","date_gmt":"2026-08-14T09:23:23","guid":{"rendered":"https:\/\/www.hostrunway.com\/blog\/?p=1284"},"modified":"2026-07-22T16:20:13","modified_gmt":"2026-07-22T16:20:13","slug":"llama-405b-deepseek-best-gpus-compared","status":"publish","type":"post","link":"https:\/\/www.hostrunway.com\/blog\/llama-405b-deepseek-best-gpus-compared\/","title":{"rendered":"Llama 405B vs DeepSeek: Which Cloud GPU Actually Handles Both in 2026?"},"content":{"rendered":"\n

Nobody warns you about the real problem with Llama 405B inference GPU<\/strong> setups until after it happens. The model does not run slow. It does not load at all. 405 billion parameters hit a memory wall and stop there. The GPU<\/a> log shows an out-of-memory error before a single token is generated.<\/p>\n\n\n\n

That is the experience teams hit every week.<\/p>\n\n\n\n

DeepSeek inference requirements<\/strong> sit in similar territory. DeepSeek V3 carries 671 billion total parameters in a Mixture-of-Experts (MoE) design. Smart architecture, but the weights still need physical memory to live in.<\/p>\n\n\n\n

This guide skips the vague cloud recommendations. It gives CTOs, ML engineers, and DevOps teams the specific numbers, real tradeoffs, and a clear path from model requirements to production hardware.<\/p>\n\n\n\n

Also Read: Cloud GPU for Beginners: Complete Step-by-Step Guide 2026<\/a><\/p>\n\n\n\n

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