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.

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 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

   Details

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

Installation

First, install ModelOpt. You can either install it directly or as an optional SGLang dependency:

# Option 1: Install ModelOpt directly
pip install nvidia-modelopt

# Option 2: Install SGLang with ModelOpt support (recommended)
pip install sglang[modelopt]

Quantization and Export Workflow

SGLang provides an example script that demonstrates the complete ModelOpt quantization and export workflow:

# 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
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:

import sglang as sgl

# Deploy exported ModelOpt quantized model
llm = sgl.Engine(
    model_path="./quantized_tinyllama_fp8",
    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.outputs[0].text}")

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:

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
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

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)
• Torchao: PyTorch Architecture Optimization
• vLLM Quantization
• auto-round