Quantization

SGLang supports various quantization methods, including offline quantization and online dynamic quantization.

Offline quantization loads pre-quantized model weights directly during inference. This is required for quantization methods such as GPTQ and AWQ, which collect and pre-compute various statistics from the original weights using the calibration dataset.

Online quantization dynamically computes scaling parameters—such as the maximum/minimum values of model weights—during runtime. Like NVIDIA FP8 training’s delayed scaling mechanism, online quantization calculates the appropriate scaling factors on-the-fly to convert high-precision weights into a lower-precision format.

Note: For better performance, usability and convenience, offline quantization is recommended over online quantization.

If you use a pre-quantized model, do not add --quantization to enable online quantization at the same time. For popular pre-quantized models, please visit Unsloth, NVIDIA ModelOpt or NeuralMagic collections on HF for some popular quality validated quantized models. Quantized models must be validated via benchmarks post-quantization to guard against abnormal quantization loss regressions.

Platform Compatibility

The following table summarizes quantization method support across NVIDIA and AMD GPUs, Ascend NPUs.

Method	NVIDIA GPUs	AMD GPUs (MI300X/MI325X/MI350X)	Ascend NPUs (A2/A3)	Notes
fp8	Yes	Yes	WIP	Aiter or Triton backend on AMD
mxfp4	Yes	Yes	WIP	Requires CDNA3/CDNA4 with MXFP support; uses Aiter
blockwise_int8	Yes	Yes	No	Triton-based, works on both platforms
w8a8_int8	Yes	Yes	No
w8a8_fp8	Yes	Yes	No	Aiter or Triton FP8 on AMD
awq	Yes	Yes	Yes	Uses Triton dequantize on AMD (vs. optimized CUDA kernels on NVIDIA). Uses CANN kernels on Ascend
gptq	Yes	Yes	Yes	Uses Triton or vLLM kernels on AMD. Uses CANN kernels on Ascend
compressed-tensors	Yes	Yes	Partial	Aiter paths for FP8/MoE on AMD. Uses CANN kernels on Ascend, FP8 not supported yet
quark	Yes	Yes	No	AMD Quark quantization; Aiter GEMM paths on AMD
auto-round	Yes	Yes	Partial	Platform-agnostic (Intel auto-round). Uses CANN kernels on Ascend
quark_int4fp8_moe	No	Yes	No	AMD-only; online INT4-to-FP8 MoE quantization (CDNA3/CDNA4)
awq_marlin	Yes	No	No	Marlin kernels are CUDA-only
gptq_marlin	Yes	No	No	Marlin kernels are CUDA-only
gguf	Yes	No	WIP	CUDA-only kernels in sgl-kernel
modelopt / modelopt_fp8	Yes (Hopper/SM90+)	No	No	NVIDIA ModelOpt; requires NVIDIA hardware
modelopt_fp4	Yes (Blackwell/SM100+)	No	No	NVIDIA ModelOpt; native FP4 on Blackwell (B200, GB200)
petit_nvfp4	No	Yes (MI250/MI300X/MI325X)	No	Enables NVFP4 on ROCm via Petit; use modelopt_fp4 on NVIDIA Blackwell. Auto-selected when loading NVFP4 models on AMD. See LMSYS blog and AMD ROCm blog.
bitsandbytes	Yes	Experimental	No	Depends on bitsandbytes ROCm support
torchao (int4wo, etc.)	Yes	Partial	No	int4wo not supported on AMD; other methods may work
modelslim	No	No	Yes	Ascend quantization; Uses CANN kernels

On AMD, several of these methods use Aiter for acceleration – set SGLANG_USE_AITER=1 where noted. See AMD GPU setup for installation and configuration details.

On Ascend, various layers quantization configurations are supported, see Ascend NPU quantization for details.

GEMM Backends for FP4/FP8 Quantization

Note

Backend selection is supported only for blockwise FP8 and NVFP4 GEMM. When running FP8 or FP4 quantized models, you can select the GEMM backend via --fp8-gemm-backend and --fp4-gemm-backend.

--fp8-gemm-backend (Blockwise FP8 GEMM)

Backend	Hardware	Description
auto	All	Auto-selects based on hardware
deep_gemm	SM90, SM100	JIT-compiled; enabled when DeepGEMM is installed
flashinfer_trtllm	SM100	FlashInfer TensorRT-LLM backend; optimal for low-latency
flashinfer_cutlass	SM100/120	FlashInfer CUTLASS groupwise FP8 GEMM
flashinfer_deepgemm	SM90	Uses swapAB optimization for small M dimensions in decoding
cutlass	SM90, SM100/120	sgl-kernel CUTLASS
triton	All	Fallback; widely compatible
aiter	ROCm	AMD AITER backend

auto selection order: 1) DeepGEMM (SM90/SM100, installed); 2) FlashInfer TRTLLM (SM100, FlashInfer available); 3) CUTLASS (SM90/SM100/120); 4) AITER (AMD); 5) Triton. Exception: SM120 always resolves to Triton.

--fp4-gemm-backend (NVFP4 GEMM)

Backend	Hardware	Description
auto	SM100/120	Auto-selects: flashinfer_cudnn on SM120; flashinfer_cutlass on SM100
cutlass	SM100/120	SGLang CUTLASS kernel
flashinfer_cutlass	SM100/120	FlashInfer CUTLASS backend
flashinfer_cudnn	SM100/120 (CUDA 13+, cuDNN 9.15+)	FlashInfer cuDNN backend; used on SM120 for performance
flashinfer_trtllm	SM100	FlashInfer TensorRT-LLM backend

When FlashInfer is unavailable for NVFP4, the SGLang CUTLASS kernel is used as an automatic fallback.

Offline Quantization

To load already quantized models, simply load the model weights and config. Again, if the model has been quantized offline, there’s no need to add --quantization argument when starting the engine. The quantization method will be parsed from the downloaded Hugging Face or msModelSlim config. For example, DeepSeek V3/R1 models are already in FP8, so do not add redundant parameters.

python3 -m sglang.launch_server \
    --model-path hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 \
    --port 30000 --host 0.0.0.0

Take note, if your model is per-channel quantized (INT8 or FP8) with per-token dynamic quantization activation, you can opt to include --quantization w8a8_int8 or --quantization w8a8_fp8 to invoke the corresponding CUTLASS int8_kernel or fp8_kernel in sgl-kernel. This action will ignore the Hugging Face config’s quantization settings. For instance, with neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8-dynamic, if you execute with --quantization w8a8_fp8, the system will use the W8A8Fp8Config from SGLang to invoke the sgl-kernel, rather than the CompressedTensorsConfig for vLLM kernels.

python3 -m sglang.launch_server \
    --model-path neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8-dynamic \
    --quantization w8a8_fp8 \
    --port 30000 --host 0.0.0.0

Examples of Offline Model Quantization

Using Unsloth

We strongly suggest the use of Unsloth to quantize and load the model. Please refer to SGLang Deployment & Inference Guide with Unsloth.

Using auto-round

# Install
pip install auto-round

• LLM quantization

# for LLM
from auto_round import AutoRound
model_id = "meta-llama/Llama-3.2-1B-Instruct"
quant_path = "Llama-3.2-1B-Instruct-autoround-4bit"
# Scheme examples: "W2A16", "W3A16", "W4A16", "W8A16", "NVFP4", "MXFP4" (no real kernels), "GGUF:Q4_K_M", etc.
scheme = "W4A16"
format = "auto_round"
autoround = AutoRound(model_id, scheme=scheme)
autoround.quantize_and_save(quant_path, format=format) # quantize and save

• VLM quantization

# for VLMs
from auto_round import AutoRoundMLLM
model_name = "Qwen/Qwen2-VL-2B-Instruct"
quant_path = "Qwen2-VL-2B-Instruct-autoround-4bit"
scheme = "W4A16"
format = "auto_round"
autoround = AutoRoundMLLM(model_name, scheme)
autoround.quantize_and_save(quant_path, format=format) # quantize and save

• Command Line Usage (Gaudi/CPU/Intel GPU/CUDA)

auto-round \
    --model meta-llama/Llama-3.2-1B-Instruct \
    --bits 4 \
    --group_size 128 \
    --format "auto_round" \
    --output_dir ./tmp_autoround

• known issues

Several limitations currently affect offline quantized model loading in sglang, These issues might be resolved in future updates of sglang. If you experience any problems, consider using Hugging Face Transformers as an alternative.

1. Mixed-bit Quantization Limitations

   Mixed-bit quantization is not fully supported. Due to vLLM’s layer fusion (e.g., QKV fusion), applying different bit-widths to components within the same fused layer can lead to compatibility issues.

2. Limited Support for Quantized MoE Models

   Quantized MoE models may encounter inference issues due to kernel limitations (e.g., lack of support for mlp.gate layer quantization). please try to skip quantizing these layers to avoid such errors.

3. Limited Support for Quantized VLMs

   VLM failure cases

   Qwen2.5-VL-7B

   auto_round:auto_gptq format: Accuracy is close to zero.

   GPTQ format: Fails with:

   The output size is not aligned with the quantized weight shape
   

   auto_round:auto_awq and AWQ format: These work as expected.

Using GPTQModel

# install
pip install gptqmodel --no-build-isolation -v

from datasets import load_dataset
from gptqmodel import GPTQModel, QuantizeConfig

model_id = "meta-llama/Llama-3.2-1B-Instruct"
quant_path = "Llama-3.2-1B-Instruct-gptqmodel-4bit"

calibration_dataset = load_dataset(
    "allenai/c4", data_files="en/c4-train.00001-of-01024.json.gz",
    split="train"
  ).select(range(1024))["text"]

quant_config = QuantizeConfig(bits=4, group_size=128) # quantization config
model = GPTQModel.load(model_id, quant_config) # load model

model.quantize(calibration_dataset, batch_size=2) # quantize
model.save(quant_path) # save model

Using LLM Compressor

# install
pip install llmcompressor

Here, we take quantize meta-llama/Meta-Llama-3-8B-Instruct to FP8 as an example to elaborate on how to do offline quantization.

from transformers import AutoTokenizer
from llmcompressor.transformers import SparseAutoModelForCausalLM
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

# Step 1: Load the original model.
MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"

model = SparseAutoModelForCausalLM.from_pretrained(
  MODEL_ID, device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# Step 2: Perform offline quantization.
# Step 2.1: Configure the simple PTQ quantization.
recipe = QuantizationModifier(
  targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])

# Step 2.2: Apply the quantization algorithm.
oneshot(model=model, recipe=recipe)

# Step 3: Save the model.
SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
model.save_pretrained(SAVE_DIR)
tokenizer.save_pretrained(SAVE_DIR)

Then, you can directly use the quantized model with SGLang, by using the following command:

python3 -m sglang.launch_server \
    --model-path $PWD/Meta-Llama-3-8B-Instruct-FP8-Dynamic \
    --port 30000 --host 0.0.0.0

Using NVIDIA ModelOpt

NVIDIA Model Optimizer (ModelOpt) provides advanced quantization techniques optimized for NVIDIA hardware.

Offline vs. Online Quantization:

SGLang supports two modes for ModelOpt.

• Offline Quantization (pre-quantized):

  • Usage: Download a pre-quantized model from Hugging Face or run hf_ptq.py once to create a new quantized checkpoint. Then load this quantized checkpoint.

  • Pros: Fast server startup, quantization can be validated before deployment, efficient resource usage.

  • Cons: Requires an extra preparation step.

• Online Quantization (quant and serve):

  • Usage: Load a standard BF16/FP16 model and add a flag. The engine applies quantization on startup.

  • Pros: Convenient (no new checkpoint needed).

  • Cons: High startup time, increases VRAM usage during initialization (risk of OOM).

The following sections guide you through using the Offline path: loading pre-quantized models or creating your own checkpoints.

Using Pre-Quantized Checkpoints

If a model is already quantized (e.g., from Hugging Face), you can load it directly.

• FP8 Models: Use --quantization modelopt_fp8.

  python3 -m sglang.launch_server \
      --model-path nvidia/Llama-3.1-8B-Instruct-FP8 \
      --quantization modelopt_fp8 \
      --port 30000
  

• FP4 Models: Use --quantization modelopt_fp4.

  python3 -m sglang.launch_server \
      --model-path nvidia/Llama-3.3-70B-Instruct-NVFP4 \
      --quantization modelopt_fp4 \
      --port 30000
  

Creating Your Own Quantized Checkpoints

If a pre-quantized checkpoint is not available for your model, you can create one using NVIDIA Model Optimizer’s hf_ptq.py script.

Why quantize?

• Reduce VRAM usage

• Higher throughput and lower latency

• More flexible deployment (on smaller GPUs)

What can be quantized?

• The entire model

• MLP layers only

• KV cache

Key options in hf_ptq.py:

--qformat: Quantization formats fp8, nvfp4, nvfp4_mlp_only

--kv_cache_qformat: KV cache quantization format (default: fp8)

Note: The default kv_cache_qformat may not be optimal for all use cases. Consider setting this explicitly.

Hardware requirements: Hopper and higher are recommended. Insufficient GPU memory may cause weight offloading, resulting in extremely long quantization time.

For detailed usage and supported model architectures, see NVIDIA Model Optimizer LLM PTQ.

SGLang includes a streamlined workflow for quantizing models with ModelOpt and automatically exporting them for deployment.

Installation

First, install ModelOpt:

pip install nvidia-modelopt

Quantization and Export Workflow

SGLang provides an example script that demonstrates the complete ModelOpt quantization and export workflow. Run from the SGLang repository root (see modelopt_quantize_and_export.py):

# Quantize and export a model using ModelOpt FP8 quantization
python examples/usage/modelopt_quantize_and_export.py quantize \
    --model-path TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
    --export-dir ./quantized_tinyllama_fp8 \
    --quantization-method modelopt_fp8

# For FP4 quantization (requires Blackwell GPU)
python examples/usage/modelopt_quantize_and_export.py quantize \
    --model-path TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
    --export-dir ./quantized_tinyllama_fp4 \
    --quantization-method modelopt_fp4

Available Quantization Methods

• modelopt_fp8: FP8 quantization with optimal performance on NVIDIA Hopper and Blackwell GPUs

• modelopt_fp4: FP4 quantization with optimal performance on Nvidia Blackwell GPUs

Python API Usage

You can also use ModelOpt quantization programmatically:

import sglang as sgl
from sglang.srt.configs.device_config import DeviceConfig
from sglang.srt.configs.load_config import LoadConfig
from sglang.srt.configs.model_config import ModelConfig
from sglang.srt.model_loader.loader import get_model_loader

# Configure model with ModelOpt quantization and export
model_config = ModelConfig(
    model_path="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    quantization="modelopt_fp8",  # or "modelopt_fp4"
    trust_remote_code=True,
)

load_config = LoadConfig(
    modelopt_export_path="./exported_model",
    modelopt_checkpoint_save_path="./checkpoint.pth",  # optional, fake quantized checkpoint
)
device_config = DeviceConfig(device="cuda")

# Load and quantize the model (export happens automatically)
model_loader = get_model_loader(load_config, model_config)
quantized_model = model_loader.load_model(
    model_config=model_config,
    device_config=device_config,
)

Deploying Quantized Models

After quantization and export, you can deploy the model with SGLang:

# Deploy the exported quantized model
python -m sglang.launch_server \
    --model-path ./quantized_tinyllama_fp8 \
    --quantization modelopt \
    --port 30000 --host 0.0.0.0

Or using the Python API (use the same path as modelopt_export_path from the quantize step):

import sglang as sgl

def main():
    # Deploy exported ModelOpt quantized model
    # Path must match modelopt_export_path from quantize step (e.g., ./exported_model)
    llm = sgl.Engine(
        model_path="./exported_model",
        quantization="modelopt",
    )

    # Run inference
    prompts = [
        "Hello, how are you?",
        "What is the capital of France?",
    ]
    sampling_params = {
        "temperature": 0.8,
        "top_p": 0.95,
        "max_new_tokens": 100,
    }

    outputs = llm.generate(prompts, sampling_params)

    for i, output in enumerate(outputs):
        print(f"Prompt: {prompts[i]}")
        print(f"Output: {output['text']}")

if __name__ == "__main__":
    main()

Advanced Features

Checkpoint Management: Save and restore fake quantized checkpoints for reuse:

# Save the fake quantized checkpoint during quantization
python examples/usage/modelopt_quantize_and_export.py quantize \
    --model-path meta-llama/Llama-3.2-1B-Instruct \
    --export-dir ./quantized_model \
    --quantization-method modelopt_fp8 \
    --checkpoint-save-path ./my_checkpoint.pth

# The checkpoint can be reused for future quantization runs and skip calibration

Export-only Workflow: If you have a pre-existing fake quantized ModelOpt checkpoint, you can export it directly. See LoadConfig for the full API:

from sglang.srt.configs.device_config import DeviceConfig
from sglang.srt.configs.load_config import LoadConfig
from sglang.srt.configs.model_config import ModelConfig
from sglang.srt.model_loader.loader import get_model_loader

model_config = ModelConfig(
    model_path="meta-llama/Llama-3.2-1B-Instruct",
    quantization="modelopt_fp8",
    trust_remote_code=True,
)

load_config = LoadConfig(
    modelopt_checkpoint_restore_path="./my_checkpoint.pth",
    modelopt_export_path="./exported_model",
)

# Load and export the model (DeviceConfig defaults to device="cuda")
model_loader = get_model_loader(load_config, model_config)
model_loader.load_model(model_config=model_config, device_config=DeviceConfig())

Benefits of ModelOpt

• Hardware Optimization: Specifically optimized for NVIDIA GPU architectures

• Advanced Quantization: Supports cutting-edge FP8 and FP4 quantization techniques

• Seamless Integration: Automatic export to HuggingFace format for easy deployment

• Calibration-based: Uses calibration datasets for optimal quantization quality

• Production Ready: Enterprise-grade quantization with NVIDIA support

Using ModelSlim

MindStudio-ModelSlim (msModelSlim) is a model offline quantization compression tool launched by MindStudio and optimized for Ascend hardware.

• Installation

  # Clone repo and install msmodelslim:
  git clone https://gitcode.com/Ascend/msmodelslim.git
  cd msmodelslim
  bash install.sh
  

• LLM quantization

  Download the original floating-point weights of the large model. Taking Qwen3-32B as an example, you can go to Qwen3-32B to obtain the original model weights. Then install other dependencies (related to the model, refer to the huggingface model card).

  Note: You can find pre-quantized validated models on modelscope/Eco-Tech.

  Traditional quantification methods require the preparation of calibration data files (.jsonl formats) for calibration in the quantification process.

  Qwen3-32B/      # floating-point model downloaded from official HF (or modelscope) repo
  msmodelslim/    # msmodelslim repo
    |----- lab_calib # calibration date folder (put your dataset here in ```.jsonl``` format or use pre-prepared ones)
        |----- some file (such as laos_calib.jsonl)
    |----- lab_practice # best practice folder with configs for quantization
        |----- model folder (such as qwen3_5_moe folder) # folder with quantization configs
            |----- quant_config (such as qwen3_5_moe_w8a8.yaml) # quantization config
    |----- another folders
  output_folder/   # generated by below command
    |----- quant_model_weights-00001-of-0001.safetensors # quantized weights
    |----- quant_model_description.json # file with description of the quantization methods for each layer (```W4A4_DYNAMIC```, etc.)
    |----- another files (such as config.json, tokenizer.json, etc.)
  

  Run quantization using one-click quantization (recommended):

  msmodelslim quant \
  --model_path ${MODEL_PATH} \
  --save_path ${SAVE_PATH} \
  --device npu:0,1 \
  --model_type Qwen3-32B \
  --quant_type w8a8 \
  --trust_remote_code True
  

• Usage Example

  python3 -m sglang.launch_server \
  --model-path $PWD/Qwen3-32B-w8a8 \
  --port 30000 --host 0.0.0.0
  

• Available Quantization Methods:

  • [x] W4A4_DYNAMIC linear with online quantization of activations

  • [x] W8A8 linear with offline quantization of activations

  • [x] W8A8_DYNAMIC linear with online quantization of activations

  • [x] W4A4_DYNAMIC MOE with online quantization of activations

  • [x] W4A8_DYNAMIC MOE with online quantization of activations

  • [x] W8A8_DYNAMIC MOE with online quantization of activations

  • [ ] W4A8 linear TBD

  • [ ] W4A16 linear TBD

  • [ ] W48A16 linear TBD

  • [ ] W4A16 MoE in progress

  • [ ] W8A16 MoE in progress

  • [ ] KV Cache in progress

  • [ ] Attention in progress

For more detailed examples of quantization of models, as well as information about their support, see the examples section in ModelSLim repo.

Online Quantization

To enable online quantization, you can simply specify --quantization in the command line. For example, you can launch the server with the following command to enable FP8 quantization for model meta-llama/Meta-Llama-3.1-8B-Instruct:

python3 -m sglang.launch_server \
    --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \
    --quantization fp8 \
    --port 30000 --host 0.0.0.0

Our team is working on supporting more online quantization methods. SGLang will soon support methods including but not limited to ["awq", "gptq", "marlin", "gptq_marlin", "awq_marlin", "bitsandbytes", "gguf"].

torchao online quantization method

SGLang also supports quantization methods based on torchao. You can simply specify --torchao-config in the command line to support this feature. For example, if you want to enable int4wo-128 for model meta-llama/Meta-Llama-3.1-8B-Instruct, you can launch the server with the following command:

python3 -m sglang.launch_server \
    --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \
    --torchao-config int4wo-128 \
    --port 30000 --host 0.0.0.0

SGLang supports the following quantization methods based on torchao ["int8dq", "int8wo", "fp8wo", "fp8dq-per_tensor", "fp8dq-per_row", "int4wo-32", "int4wo-64", "int4wo-128", "int4wo-256"].

Note: According to this issue, "int8dq" method currently has some bugs when using together with cuda graph capture. So we suggest to disable cuda graph capture when using "int8dq" method. Namely, please use the following command:

python3 -m sglang.launch_server \
    --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \
    --torchao-config int8dq \
    --disable-cuda-graph \
    --port 30000 --host 0.0.0.0

quark_int4fp8_moe online quantization method

SGLang running on AMD GPUs (CDNA3 or CDNA4 architecture) supports the quantization method --quantization quark_int4fp8_moe, that will replace MoE layers originally in high precision (bfloat16, float16 or float32) to use weights dynamically quantized to int4, that are upcasted to float8 during inference to run compute in float8 precision with activations dynamically quantized on the fly to float8.

Other layers (e.g. projections in the attention layers) have their weights quantized online to float8 directly.

Reference

• GPTQModel

• LLM Compressor

• NVIDIA Model Optimizer (ModelOpt)

• NVIDIA Model Optimizer LLM PTQ

• Petit: NVFP4 on ROCm — LMSYS blog, AMD ROCm blog

• Torchao: PyTorch Architecture Optimization

• vLLM Quantization

• auto-round

• ModelSlim