AI Infrastructure
Model Quantization Guide: AWQ vs GPTQ vs GGUF for 4-Bit & 8-Bit Inference
Compress 70B parameter models from 140GB down to 38GB VRAM with Activation-aware Weight Quantization (AWQ) while preserving reasoning accuracy.
AWQ vs GPTQ
AWQ protects the critical top 1% salient weight channels, maintaining near-lossless perplexity at 4-bit quantization.
Related Technical Guides
Deepen your understanding with these closely related production architectures and tutorials:
Llama 4 MoE & DeepSeek-R1: 24GB GPU Hardware Limits, vLLM PagedAttention & think Filtering
Deploy DeepSeek-R1 distilled weights and evaluate Meta Llama 4 Scout/Maverick MoE hardware realities on single 24GB GPUs with vLLM and FastAPI.
DeepSeek-R1 & Open-Source Reasoning: Self-Hosting with Ollama, vLLM, and Enterprise GPU Deployment
Deploy DeepSeek-R1 and distilled open-weight reasoning models locally with vLLM or Ollama for zero-API-cost private reasoning engines and air-gapped data privacy.
Multi-Model Management with LiteLLM: Unified APIs, Automatic Fallback & Load Balancing
Combine OpenAI, Anthropic, Gemini, Bedrock, and local models under a single standardized interface with automatic retry and rate-limit routing.