# Accelerate Your AI Workflow with FP4 Quantization on Lambda

July 16, 2025• 8 min read

As AI models grow in complexity and size, the demand for efficient computation becomes paramount. FP4 (4-bit Floating Point) precision emerges as a transformative approach, balancing performance with resource optimization.

## Understanding FP4 Precision

FP4 precision represents numerical values using 4 bits: 1 bit for the sign, 2 bits for the exponent and 1 bit for the mantissa. This structure reduces the numerical representation size, thus shrinking model memory footprints and computational overhead.

With this configuration, FP4 values typically represent numbers within a dynamic range capable of encoding values between ±6.0; offering a balanced trade-off between numerical range and precision. This ultra-low-bit quantization technique accelerates data processing and significantly reduces VRAM consumption, enabling faster processing and reduced memory usage.

## Benefits of FP4 in AI Models

1. **Reduced Memory Footprint**: Quantizing models to FP4 lowers memory footprint. For example, the Qwen3-32B model's memory requirement drops from **64GB in BF16 to 24GB** when quantized to FP4, enabling easier deployment.
2. **Increased Throughput**: Quantizing models to FP4 precision substantially boosts inference speed. For instance, the FLUX model achieved approximately a **3x increase in throughput** when quantized from FP16 to FP4, significantly accelerating inference performance.
3. **Energy Efficiency**: Lower computational demands translate to reduced energy consumption.
4. **Scalability**: FP4 enables deployment of complex models on hardware with limited resources.

## Implementing FP4 in Practice

Adopting FP4 precision involves quantizing existing models through techniques like Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT). Tools such as NVIDIA TensorRT™ facilitate this transition, ensuring models maintain high accuracy post-quantization.

#### Code Example: Quantizing GPT-2 to FP4 Using NVIDIA TensorRT on Lambda’s 1-Click Clusters

Here's a working example demonstrating how to quantize Hugging Face's GPT-2 model to FP4 precision using NVIDIA TensorRT Model Optimizer, specifically optimized for deployment on Lambda's 1-Click Clusters.

Step 1: Set up your Lambda Cluster environment:

Log into your Lambda gpu-cloud [account](https://auth.lambdalabs.com/u/login/identifier?state=hKFo2SBXWkpNbzM1bW9KcmFNckpwVDhoN2xGczZWZk1lNkd1aqFur3VuaXZlcnNhbC1sb2dpbqN0aWTZIENDOUoybEdTdHktUmRmQmhFMXA0OTM2WXMxTFVFek9Po2NpZNkgdFZ3U1o5QVk0S21RcUJDbTAyYXRkaThXVEtKMHlWV00) and navigate to [1-Click Clusters](https://cloud.lambda.ai/one-click-clusters/running). Make sure [Lambda Stack](https://lambda.ai/lambda-stack-deep-learning-software) is installed. TensorRT is pre-installed on Lambda's NVIDIA GPU instances.

```
# SSH into your Lambda 1-Click cluster
ssh user@your-lambda-instance-ip

# Activate your Python environment (optional but recommended)
python3 -m venv env
source env/bin/activate

# Install required packages
pip install transformers torch numpy
```

Step 2: Export GPT-2 model to ONNX (needed for TensorRT)

```
import torch
from transformers import GPT2Model, GPT2Tokenizer

model_name = 'gpt2'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2Model.from_pretrained(model_name)

dummy_input = tokenizer.encode("Hello world!", return_tensors="pt")

torch.onnx.export(
    model,
    dummy_input,
    "gpt2.onnx",
    input_names=['input_ids'],
    output_names=['output'],
    opset_version=13
)
```

Step 3: Quantize to FP4 using TensorRT (experimental and limited)

```
trtexec \
    --onnx=gpt2.onnx \
    --fp4 \
    --saveEngine=gpt2_fp4.engine
```

This would convert your ONNX model into an optimized FP4 TensorRT engine.

Step 4: Running inference on FP4 quantized TensorRT model

```
import tensorrt as trt
import numpy as np
import pycuda.driver as cuda
import pycuda.autoinit

TRT_LOGGER = trt.Logger(trt.Logger.WARNING)

def load_engine(engine_path):
    runtime = trt.Runtime(TRT_LOGGER)
    with open(engine_path, "rb") as f:
        return runtime.deserialize_cuda_engine(f.read())

engine = load_engine("gpt2_fp4.engine")
context = engine.create_execution_context()

# Example input
input_ids = np.array([[15496, 995]], dtype=np.int32)  # Tokens for "Hello world!"

# Allocate input/output buffers
input_buffer = cuda.mem_alloc(input_ids.nbytes)
output_size = trt.volume(engine.get_binding_shape(1)) * np.dtype(np.float32).itemsize
output_buffer = cuda.mem_alloc(output_size)

cuda.memcpy_htod(input_buffer, input_ids)

# Execute FP4 quantized inference
context.execute_v2(bindings=[int(input_buffer), int(output_buffer)])

# Retrieve results
output = np.empty(trt.volume(engine.get_binding_shape(1)), dtype=np.float32)
cuda.memcpy_dtoh(output, output_buffer)

print("Inference output:", output)
```

## Case Study: FLUX Model Optimization

The FLUX model exemplifies FP4's potential. By quantizing the transformer backbone to FP4, there is a 3x increase in throughput and a ~60% reduction in VRAM usage compared to FP16, all while maintaining image quality.

| System | No. of GPUs | Quantization | Latency(ms) | Throughput <br>Speedup vs H100 FP16 |
| --- | --- | --- | --- | --- |
| NVIDIA HGX H100 | 8 | FP16 | 1.00x (baseline) | 1.00x (baseline) |
| NVIDIA HGX H100 | 8 | FP8 | 1.4x faster | 1.46x |
| NVIDIA HGX B200 | 8 | FP8 | 2.63x faster | 2.80x |
| NVIDIA HGX B200 | 8 | FP4 | 3.8x faster | 3.17x |

## Conclusion

FP4 precision stands at the forefront of AI model optimization, offering a path to more efficient, scalable, and accessible AI solutions. By reducing computational demands without sacrificing performance, FP4 paves the way for broader adoption of advanced AI technologies.