LLM VRAM Needs: GigaChat 3.5, GLM-5.2, Kimi K3 in 4-bit

The article details the VRAM requirements for running large language models like GigaChat 3.5 Ultra, GLM-5.2, and Kimi K3 in July 2026, focusing on 4-bit quantization. It explains that the "4-bit" designation is a simplification, with actual average bit usage being higher due to quantization methods like Q4_K_M, which averages 4.85 bits per weight. The text provides a formula for calculating VRAM needs, considering both model weights and KV cache, and highlights that models with very large context windows, such as Kimi K3 and GLM-5.2, require substantial memory for their KV cache. Practical VRAM estimates are given for GigaChat 3.5 Ultra (220-250 GB for 4-bit) and GLM-5.2 (372-475 GB for 4-bit, or ~245 GB for 2-bit), noting that running these models on consumer hardware is challenging. AI

IMPACT Understanding VRAM requirements is crucial for deploying and running large language models efficiently on available hardware.

RANK_REASON The article discusses technical details and VRAM requirements for running specific LLMs, akin to a technical deep-dive or benchmark analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• Anthropic
• Claude
• GPT
• GigaChat 3.5 Ultra
• GLM-5.2
• Hugging Face
• Kimi K3
• llama.cpp
• Ollama
• OpenAI
• Unsloth
• Z.ai

• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →