Q4_K_M or AWQ? The Quantization Decision That Fits a 70B Model Into Your Existing GPU Budget

TL;DR: A 70B model quantized to Q4_K_M typically outperforms a 7B at full precision — here is the format decision that fits more intelligence into the GPU budget you already have.

What you will be able to do after this: Choose the right quantization format for your hardware, convert any HuggingFace model to GGUF with llama.cpp, and run it locally or serve it at production throughput with AWQ on vLLM.

Three things to take away:

• GGUF Q4_K_M is the universal starting point — runs on CPU, Apple Silicon, NVIDIA, and AMD with ~92% of FP16 quality at 3-4x compression
• AWQ + vLLM + Marlin kernel is for NVIDIA production: 741 tok/s vs 461 tok/s for FP16 on a 70B Qwen2.5 (per independent benchmark)
• bitsandbytes is for training only (QLoRA fine-tuning); at 168 tok/s, it is 4x slower than AWQ and the wrong choice for inference

How to pick:

Your setup	Format	Variant
Ollama / LM Studio / CPU	GGUF	Q4_K_M (default)
NVIDIA GPU, production	AWQ	4-bit via vLLM
Fine-tuning (QLoRA)	bitsandbytes	load_in_4bit=True

Convert a HuggingFace model to GGUF:

# Build llama.cpp
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp
cmake -B build && cmake --build build --config Release -j

# Step 1: HF model -> F16 GGUF
python convert_hf_to_gguf.py /path/to/model --outfile model-f16.gguf --outtype f16

# Step 2: quantize to 4-bit
./build/bin/llama-quantize model-f16.gguf model-q4_k_m.gguf Q4_K_M

# Step 3: run it
./build/bin/llama-cli -m model-q4_k_m.gguf -p "Explain quantization briefly." -n 80

If the model already has GGUF variants on HuggingFace, skip the build step entirely:

ollama run hf.co/bartowski/Qwen2.5-72B-Instruct-GGUF:Q4_K_M

Use Q5_K_M if you have 6-8 GB of headroom and want higher fidelity. Stay at Q4 or above for models 70B and up — Q3 and below produce noticeable reasoning quality loss.

Where it breaks:

• AWQ without the Marlin kernel runs slower than FP16 baseline — verify your vLLM version includes Marlin support before using AWQ in production
• Quantization formats cannot be losslessly converted between each other; download the format you need from the start
• Peak memory during GGUF conversion is roughly 1.5x the final model size, not the quantized size — plan disk and RAM accordingly
• Some reasoning-heavy models are more quantization-sensitive than others; test on your specific task before committing to a format

Sources: GGUF Quantization Tutorial: Run Fine-Tuned LLMs on CPU with llama.cpp (video walk-through), LLM Quantization Guide: GGUF vs AWQ vs GPTQ vs bitsandbytes — benchmarks (PremAI, independent), llama.cpp quantize README (official docs)