HyperCLOVAX-SEED-Text-Instruct-1.5B-Q4_K_M GGUF size and VRAM requirements

HyperCLOVAX-SEED-Text-Instruct-1.5B-Q4_K_M-GGUF is a compact instruction-tuned chat model focused on general purpose & instruction. With approximately 1.81 billion parameters, it balances local hardware requirements with reasoning and generation fidelity. It is built on the llama architecture with a native context window of up to 131,072 tokens. The model is primarily recommended for general knowledge, document summarization, email drafting, language translation, and multi-turn conversational chat. Released under the other license, it can be executed fully offline without sending data to external APIs.

Under the hood it uses 24 transformer layers, a hidden size of 2,048, 16 attention heads. It uses grouped-query attention (16 query heads sharing 8 key/value heads), which already trims KV-cache memory compared with full multi-head attention.

To run rippertnt/HyperCLOVAX-SEED-Text-Instruct-1.5B-Q4_K_M-GGUF locally at a 4,096-token context, its quantized versions need between 2.23 GB (Q4_K_M, lowest quality) and 2.23 GB (Q4_K_M, highest quality) of memory, weights plus KV cache and a system margin included. Context length drives that memory directly: at 4,096 tokens the KV cache for HyperCLOVAX-SEED-Text-Instruct-1.5B-Q4_K_M-GGUF is about 0.38 GB, rising to roughly 12.0 GB at its full 131,072-token context. Shorter prompts free up memory for a higher-quality quantization.

For most users the best balance is Q4_K_M, needing about 2.23 GB. That means rippertnt/HyperCLOVAX-SEED-Text-Instruct-1.5B-Q4_K_M-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.

Available GGUF quantizations for rippertnt/HyperCLOVAX-SEED-Text-Instruct-1.5B-Q4_K_M-GGUF include Q4_K_M. The model supports a native context length of up to 131,072 tokens; a longer context grows the KV cache and the memory needed.

→ Guide: How much VRAM do you need?