Why compressed-tensors

Most quantization formats (AWQ, GPTQ, GGUF) target a single inference backend and ship a frozen weight layout that cannot be further composed or modified at load time. compressed-tensors is the format developed by Neural Magic and maintained as a first-class vLLM citizen.

• Native vLLM integration. No format conversion, no plugin shims. vLLM reads compressed-tensors models directly via its built-in CompressedTensorsWorker. Full PagedAttention, continuous batching, and tensor parallelism work without modification.
• Composable precision. A single checkpoint can carry per-layer or per-group precision assignments. Mixed-precision MoE configurations are expressed in the same file.
• Reproducible calibration metadata. The quantization config, calibration scheme, and per-channel scales are stored inside the checkpoint.
• Forward compatibility. As vLLM adds new kernel support (FP8, INT8, sparse), compressed-tensors models gain that support without re-quantizing.

AWQ and GPTQ remain fine for llama.cpp and older toolchains. If you are deploying on vLLM in production, compressed-tensors is the correct choice.

Quality Standard

AutoRound at iters=200 runs sign-gradient optimization over a calibration set to minimize weight rounding error. At W4A16, this closes most of the gap between naive round-to-nearest and GPTQ/AWQ, while producing a checkpoint that vLLM can load natively.

Model Catalog

All 16 models in compressed-tensors format, validated for vLLM v0.9.0+.

Quickstart

Requires vLLM v0.9.0+ and an Ampere-class GPU (A100, A6000, RTX 3090/4090, or equivalent).

Install

pip install vllm>=0.9.0

Offline inference

from vllm import LLM, SamplingParams

llm = LLM(
    model="88plug/Qwen3.6-35B-A3B-W4A16",
    max_model_len=131072,
    tensor_parallel_size=1,
)

sampling_params = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=512)
outputs = llm.generate(["Explain W4A16 vs W8A16 tradeoffs."], sampling_params)
print(outputs[0].outputs[0].text)

OpenAI-compatible server

vllm serve 88plug/Qwen3.6-35B-A3B-W4A16 \
    --max-model-len 131072 \
    --port 8000

Hardware Requirements