It has nothing to do with local RAM usage. But a million tokens of LLM context is decidedly not 5mb. The rough estimate is 2 * L * H_kv * D * bytes per element

It has nothing to do with local RAM usage. But a million tokens of LLM context is decidedly not 5mb. The rough estimate is 2 * L * H_kv * D * bytes per element Where: * L = number of layers * H_kv = # of KV heads * D = head dimension * factor of 2 = keys + values The dominant factor here is typically 2 * H_kv * D since it’s usually at least 2048 bytes. Per token. For Llama3 7B youre looking at 128gib if you’re context is really 1M (not that that particular model supports a context so big). DeepSeek4 uses something called sparse attention so the above calculus is improved - 1M of context would use 5-10GiB. But regardless of the details, you’re off by several orders of magnitude.