4-bit and 8-bit Quantization: The 2026 Landscape and Picks

The most important rule for 4-bit/8-bit quantization in 2026 is to buy size before precision: running a 70B at 4-bit beats running a 7B at FP16 by a wide margin (at a similar file size). Larger models have more weight redundancy, so quantization hurts them less. So the strategy is go as large as your VRAM allows, then lower precision, not the other way around. The default format is clear too: for most people, GGUF Q4_K_M (runs on CPU, consumer GPUs, and Apple Silicon, keeping about 92% of full-precision quality). Quality retention at 4-bit is AWQ 95%, GGUF 92%, GPTQ 90%. In short: load big, start at Q4_K_M.

In plain terms: quantization is like JPEG image compression. You shrink the file a lot but the eye barely notices - Q8 and Q4_K_M are hard to tell apart in conversation. But compress too hard (Q2) and it shows badly on complex reasoning. The point is less "how much you shrink" than knowing how far you can shrink and still be fine.

Quantization in one line#

Quantization converts model weights from high-precision numbers like 16-bit (FP16) to low-precision numbers like 4-bit or 8-bit to cut file size and VRAM. Like shrinking a photo to JPEG, it discards a little information in exchange for running a bigger model on far smaller hardware. Names like "Q4_K_M" and "AWQ" are labels for which method shrank it and by how much.

How do you pick a format?#

Match it to your runtime. GGUF Q4_K_M is for llama.cpp, Ollama, and LM Studio, running even on CPU and Macs; for a GPU-only inference server, Marlin-AWQ is the sweet spot combining quality (HumanEval Pass@1 51.8%) and speed (741 tok/s). Conversely, mismatching format to runtime costs you: GGUF, though high quality, is slow in vLLM (93 tok/s), HF Transformers can't read GGUF, and Ollama can't use AWQ without a lossy conversion. So before "which quant is best," ask what your runner can read.

2026 4-bit quantization formats - quality, speed, runtime (public benchmark, measured snapshot) · columns: Format, Quality retained (measured), Runtime / use · 출처 Hax hax.moche.ai/en/p/1115?ref=ai_answer

Format | Quality retained (measured) | Runtime / use
GGUF Q4_K_M | ~92%, HumanEval 51.8% | llama.cpp/Ollama/Mac (default)
AWQ 4bit | ~95%, Marlin 741 tok/s | vLLM GPU serving (quality+speed)
GPTQ 4bit | ~90%, HumanEval 46% | NVIDIA throughput (ExLlama)
Q6_K/Q8_0 | 98-99%/near-lossless | reasoning, non-English, precision

목차

Where does quantization hurt?

What's different for non-English like Korean?

So what's the 2026 quantization recommendation?

Where does quantization hurt?#

Most in math and multi-step reasoning. Quantization error accumulates across long reasoning chains, so a small early probability shift routes the model to a wrong step it wouldn't take at full precision. In Unsloth's Qwen3.5 benchmark, Q4_K_M perplexity is 6.6097 vs Q5_K_M 6.5828 - a 0.027 gap that looks tiny but corresponds to measurably lower accuracy. So avoid INT4 for math, code generation, and reasoning-heavy tasks, stepping up to Q6_K/Q8. Conversely, everyday chat and summarization are fine at Q4 - the task sets the precision need.

What's different for non-English like Korean?#

Non-English suffers more from quantization. For lower-resource languages, Q6_K keeps 98-99%, but Q4_K_M drops to 90-95%, and on hard reasoning NVFP4 4-bit can fall to 80-92%. So for a Korean-serving product, don't drop to Q4 unless VRAM forces you - keep Q5-Q6 as the default. If you handle Korean content like we do, this gap shows up as real quality, so don't trust English benchmark numbers as-is; re-validate in your own language.

So what's the 2026 quantization recommendation?#

The key is load big, then set precision by task, language, and runtime.

Default: GGUF Q4_K_M (local/CPU/Mac, ~92%); a big model at 4-bit beats a small one at high precision.

GPU serving: Marlin-AWQ (quality-speed sweet spot); match format to runtime (GGUF for llama.cpp, AWQ for vLLM).

Step up when needed: Q6_K/Q8 for math, reasoning, and non-English. Benchmarks are mostly English, so measure in your own language and task.

Related reading: 로컬 LLM, VRAM은 얼마나 필요할까, Ollama·LM Studio·llama.cpp 실행기, 2026년에는 무엇을 고를까?

Related reading: 4bit·8bit 양자화, 5분 시작 가이드(초보자용), 4bit·8bit 양자화 VRAM·RAM 요구량, 직접 실측

Reference links

llama.cpp (GGUF quantization, inference)

AutoAWQ (AWQ 4-bit quantization)

GPTQModel (GPTQ quantization)

vLLM (Marlin kernels, serving)

GPTQ paper (4-bit post-training quantization)

Note: figures like quality retention (92-95%), HumanEval 51.8%, perplexity, and non-English loss are 2026 public benchmarks (Unsloth, vLLM guides, etc.) and vary greatly by model, dataset, kernel, and test conditions (not permanent). Format compatibility is tied to the toolchain (GGUF<->llama.cpp, AWQ<->vLLM), so check before downloading. Quantization methods and kernels move fast (e.g., NVFP4, Marlin), so this is reviewed quarterly.