Which .GGUF Should You Download? (Hugging Face Quantization Guide)
Stop guessing model files on Hugging Face. This video shows you which file to download for your stack—fast. We keep it practical: GGUF first (Ollama / LM Studio / llama.cpp), short side-aisles for GPTQ / AWQ / EXL2, a clear memory ladder (Q8/Q6/Q5/Q4), and when QAT (Gemma-3) gives 4-bit with bf16-like behavior—without installs or hardware detours. Perfect for users running local LLMs on Ollama, LM Studio, or llama.cpp who need to choose between Q4, Q5, Q6, Q8 quantizations. What you’ll learn → Formats by stack: GGUF vs GPTQ vs AWQ vs EXL2—which one belongs to your runtime → The Memory Ladder: Q8→Q4 heuristics you can actually feel (reasoning, JSON, long context) → Q5_K_M vs Q4_K_M: where structured outputs start to fail, and when to step up → The #1 download trap: Base vs Instruct on the Files tab—and how to avoid it → QAT in practice: when Gemma-3 QAT beats generic 4-bit for long context & strict JSON → Concrete picks: Llama 3.1 (8B) in GGUF/GPTQ/AWQ/EXL2 + where GPT-OSS fits #GGUF #HuggingFace #Quantization #LocalLLM 🔗 Model resources https://huggingface.co/bartowski/Meta... https://huggingface.co/shuyuej/Meta-L... https://huggingface.co/ilhamdprastyo/... https://huggingface.co/turboderp/Llam... https://huggingface.co/google/gemma-3... https://huggingface.co/openai/gpt-oss... https://huggingface.co/openai/gpt-oss... https://huggingface.co/unsloth/gpt-os... 🎬 More on local AI • Small Language Models Under 4GB: • Small Language Models Under 4GB: What Actu... • End of VRAM? • Will Unified Memory Kill Discrete GPUs for... • Is local AI image generation dying? • ComfyUI vs Gemini & ChatGPT: Is Local Imag... 🛠 Support the channel Patreon / nexttechandai ⏱️ CHAPTERS 00:00 Which Model File Should You Download? 00:20 Understanding Model Quantization 01:06 Format Guide: GGUF, GPTQ, AWQ, QAT 02:25 The Memory Ladder: Q8 to Q3 05:06 Reading the HuggingFace Files Tab 07:15 Advanced Options GPTQ, EXL2, AWQ, QAT 08:20 GPT-OSS & Mixture-of-Experts Specifics 09:14 What's Next: KV Compression, BitNet, Better Kernels Comment to help others: Which quant are you using, and for what (chat, coding, RAG, long context)? I’ll compile the most common picks.
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