Reducing Inference Times on LLMs by 80%

November 17, 2023


Language models like GPT-3 have revolutionized the field of natural language processing, enabling a wide range of applications such as chatbots, content generation, and language translation. However, these models can be computationally expensive to run, especially in real-time applications. In this blog post, we'll explore some strategies to significantly reduce inference times on large language models (LLMs) by up to 80%. By optimizing your LLM's performance, you can unleash its full potential while keeping latency in check.

Understanding LLM Text Generation

Before diving into optimization techniques, it's important to understand how LLMs generate text. Text generation with LLMs involves a two-step process: 'prefill' and 'decoding.' During prefill, the input tokens are processed in parallel. In the decoding step, text is generated one token at a time in an autoregressive manner. Each generated token is appended to the input, and the model uses it to predict the next token. The process continues until a special stop token is output or a user-defined condition is met.

Tokens can represent words or sub-words, and the rules for tokenization vary between LLM models. It's essential to be aware of these variations when evaluating performance metrics. For instance, tokenization for different models, like Llama 2 and ChatGPT, can have differences in length, making direct comparisons challenging.

Important Metrics for LLM Serving

To measure the performance of LLM serving, some key metrics are as follows:

  1. Time To First Token (TTFT): TTFT represents the time it takes for users to see the first model output after entering a query. Low TTFT is crucial for real-time interactions, as it minimizes user waiting time.
  2. Time Per Output Token (TPOT): TPOT measures the time it takes to generate an output token for each user. It directly impacts the perceived 'speed' of the model, with lower TPOT values indicating faster response times.
  3. Latency: Latency is the overall time it takes for the model to generate a full response for a user. It can be calculated using TTFT and TPOT and is a key metric for assessing the overall responsiveness of the model.
  4. Throughput: Throughput measures the number of output tokens generated per second by the inference server. It reflects the capacity of the system to serve multiple users and requests simultaneously.

The goal is to achieve the fastest TTFT, highest throughput, and the quickest TPOT. However, there's a tradeoff between throughput and TPOT, as processing multiple user queries concurrently can increase throughput but may lead to longer TPOT for individual users.

Challenges in LLM Inference

Optimizing LLM inference times involves addressing several challenges. Various techniques include operator fusion, quantization, compression, and parallelization. Additionally, Transformer-specific optimizations are crucial.

One such optimization is Key-Value (KV) caching, which addresses the inefficiency of the Attention mechanism in decoder-only Transformer-based models. The Attention mechanism requires tokens to attend to all previously seen tokens, resulting in redundant computations. KV caching saves intermediate keys/values for the attention layers, preventing repeated computations and improving inference speed.

Memory Bandwidth Is Key

In LLMs, most computations are dominated by matrix-matrix multiplications, which are memory-bandwidth-bound operations on most hardware. When generating tokens in an autoregressive manner, one of the activation matrix dimensions is small at small batch sizes, making the speed dependent on loading model parameters from GPU memory to local caches quickly. Memory bandwidth is a better predictor of token generation speed than peak compute performance.

Inference hardware utilization is crucial for cost efficiency. GPUs are expensive, and maximizing their usage is essential. Shared inference services aim to keep costs low by combining workloads from multiple users and batching overlapping requests. However, serving large models efficiently at scale requires a balance between batch size, KV cache size, and the number of GPUs.

Model Bandwidth Utilization (MBU)

To assess the efficiency of an LLM inference server, a new metric called Model Bandwidth Utilization (MBU) was also introduced. MBU is defined as the achieved memory bandwidth divided by the peak memory bandwidth and quantifies how effectively the system utilizes available memory bandwidth. MBU is especially valuable for comparing different inference systems in a normalized manner and is complementary to Model Flops Utilization (MFU), which is important in compute-bound settings.

MBU values close to 100% indicate efficient memory bandwidth utilization, while lower values suggest underutilization. MBU is used to explore how different degrees of tensor parallelism impact inference efficiency and to understand the trade-offs involved in selecting hardware configurations.

Comprehending the Importance of Inference Time Optimization 

Inference, the act of generating predictions or responses through a trained language model typically occurs as an API or web service. Given the resource-intensive nature of large language models (LLMs), optimizing them for efficient inference becomes crucial. Take, for instance, the GPT-3 model, boasting 175 billion parameters, equivalent to 700GB of float32 numbers. Activation demands a similar amount of memory, emphasizing that we're referring to RAM. Without employing any optimization techniques, making predictions would necessitate 16 A100 GPUs with 80GB of video memory!

A substantial reduction in inference time yields various advantages, from boosting throughput in high-traffic systems to diminishing user wait times in interactive applications. Moreover, minimizing inference time can lead to significant cost savings when deploying models on cloud platforms, where pricing often hinges on compute time.

Techniques for Inference Optimization 

Now, let's explore diverse strategies for enhancing inference time:

1. Model Pruning

The general procedure for constructing a pruned network involves three steps:

  • Converge a dense neural network through training.
  • Prune / trim the network to eliminate unwanted structures.
  • (Optional) Retrain the network to ensure the new weights retain the previous training effect.

Proper pruning can notably decrease inference time. Fewer weights or neurons result in fewer computations during the forward pass, making the model faster. This is particularly advantageous for real-time applications like autonomous driving or voice assistants.

Here is a simplified example of how you implement model pruning using Pytorch:

import torch
import torch.nn.utils.prune as prune
from transformers import BertModel

model_bert = BertModel.from_pretrained('bert-base-uncased')

model_layer = model_bert.encoder.layer[0].attention.self.query

prune.l1_unstructured(model_layer , 'weight', amount=0.25)


prune.remove(model_layer, 'weight')


2. Quantization

Model quantization involves reducing the precision of model values, such as weights, by converting floating-point numbers to lower-precision integers. This process can achieve substantial memory savings and faster computation without a significant loss of model performance. It's even possible to convert model weights to int8 with minimal precision loss. The quantization process can be expressed mathematically as:

X_int 8 = round(X_fp32 / S )+Z

where the input matrix (X_fp32), scaling factor (S), and integer zero point (Z) are, respectively.

import torch.quantization as quant

quantized_model = quant.quantize_dynamic(model_bert, {torch.nn.Linear})

3. Use of Distillation

Knowledge Distillation is a technique where a smaller model (the 'student') is trained to mimic the behavior of a larger, more complex model (the 'teacher'). The idea is to transfer the 'knowledge' of the teacher model to the student model, even if the student model has a simpler architecture.

To test these, you would need a GPU node. To get one, head over to MyAccount and sign up. Then launch a GPU node as is shown in the screenshot below:

Make sure you add your ssh keys during launch, or through the security tab after launching. 

Once you have launched a node, you can use VSCode Remote Explorer to ssh into the node and use it as a local development environment. 

Here's a condensed example of how to use PyTorch and the Hugging Face Transformers library to apply Knowledge Distillation for huge language models:

Please note that you have to provide input_data, attention_mask, and labels in the form of tensors to execute the below script.

import torch
import torch.nn as nn
from transformers import BertModel 
from transformers import BertForSequenceClassification
from transformers import AdamW

class CustomStudentModel(nn.Module):
    def _init_(self):
        super(CustomStudentModel, self)._init_()
        self.bert_model = BertModel.from_pretrained('bert-base-uncased')
        self.classifier = nn.Linear(768, 2)  

    def forward(self, input_ids, attention_mask):
        model_outputs = self.bert_model(input_ids=input_ids, attention_mask=attention_mask)
        logits = self.classifier(model_outputs.pooler_output)
        return logits

# assume pre-trained teacher model
teacher_model = BertForSequenceClassification.from_pretrained('bert-large-uncased')
custom_student_model = CustomStudentModel()

input_data = torch.tensor([...])  # Replace with actual data
attention_mask = torch.tensor([...])  # Replace with actual data
labels = torch.tensor([...])  # Replace with actual data

# forward pass through the teacher model
with torch.no_grad():
    teacher_logits = teacher_model(input_data, attention_mask=attention_mask).logits

# forward pass through the custom student model
custom_student_logits = custom_student_model(input_data, attention_mask=attention_mask)

# compute the distillation loss
distillation_loss = nn.KLDivLoss()(F.log_softmax(custom_student_logits / 2, dim=-1),F.softmax(teacher_logits / 2, dim=-1))

# add a classification loss term if desired
classification_loss = nn.CrossEntropyLoss()(custom_student_logits, labels)

# total loss
total_loss = distillation_loss + classification_loss

# backward pass and optimization
optimizer = AdamW(custom_student_model.parameters())

4. Batch Inference

When optimizing model inference time, there's often a trade-off with accuracy. Techniques like model pruning, quantization, or distillation may reduce accuracy, so it's essential to evaluate these strategies based on your application's tolerance for reduced accuracy. If applicable, employing batch inference (predicting on multiple instances simultaneously) can accelerate processing while maintaining downstream performance. For instance, the paper 'Batch Prompting: Efficient Inference with Large Language Model APIs' introduces batch prompting, a straightforward alternative prompting approach enabling LLM to conduct inference in batches. This method reduces both token and time costs while preserving downstream performance, especially under a few-shot in-context learning setting.

5. Mixed System of Experts (MoE)

A Mixture of Experts (MoE) model is an ensemble learning technique where different parts of the input data are processed by distinct models ('experts'), and their outputs are combined for the final result. The 'gating function' determines the most suitable expert for a new task. This approach embraces divide-and-conquer, enhancing information processing efficiency. While a mixed system of expert architecture can potentially enhance inference time, its primary objective typically revolves around increasing model capacity and performance rather than minimizing inference time.

Here's a simplified example of implementing a MoE model in PyTorch:

Please note that here you have to provide the input_data. This is just an overview script that can make you help build your neural network from scratch.

import torch
import torch.nn as nn
from torch.nn import functional as F
from transformers import BertModel

class CustomExpert(nn.Module):
    def _init_(self):
        super(CustomExpert, self)._init_()
        self.bert_model = BertModel.from_pretrained('bert-base-uncased')

    def forward(self, input_tensor):
        return self.bert_model(input_tensor)[0]

class CustomMoE(nn.Module):
    def _init_(self, num_experts):
        super(CustomMoE, self)._init_()
        self.experts = nn.ModuleList([CustomExpert() for _ in range(num_experts)])
        self.gating_network = nn.Linear(num_experts, num_experts)

    def forward(self, input_tensor):
        expert_weights = F.softmax(self.gating_network(input_tensor), dim=0)
        return sum(weight * expert(input_tensor) for weight, expert in zip(expert_weights, self.experts))

# creating a custom MoE model with 3 experts
custom_moe = CustomMoE(num_experts=3)

# Passing forward
# Assume that your input tensor is input_data.
output_tensor = custom_moe(input_data)

Optimization Case Study: Quantization

Quantization: A Shifting of the Rules for LLMs

The approach of quantization has become increasingly popular, particularly with the emergence of Large Language Models. We will explore quantization for LLMs in this section and see how it affects the local execution of these models. We will also explain a different approach that goes beyond quantization and further minimizes computational needs, giving you an understanding of why these methods should catch your attention. 

What Is Quantization for LLMs?

Through the process of quantization, we can shrink neural networks from their original 32-bit floating-point format to an 8-bit format, which is a lower precision format for the weights and biases of the network. The particular floating-point format may change based on the model's construction and training procedures, among other things. Reducing the size of the model is the main objective of quantization, as this lowers the amount of memory and processing power required for inference and model training.

The process of quantization can be challenging, particularly if separate model quantization is being attempted. Problems may occur because some vendors may not provide hardware support. Nonetheless, there are apps and services offered by third parties that help speed up the quantization procedure.


Quantization can significantly shorten LLM inference times when used with other techniques like altering the nature of tensor data. These methods create new opportunities to run LLMs with additional parameters at a reasonable latency. You must investigate and apply optimization approaches that are in line with your particular use case, regardless of whether you decide to use quantization, parallelism, model pruning, or other strategies. Reducing latency would not only improve the user experience but also increase the affordability and accessibility of LLMs for a variety of applications. 

Key Results

Optimizing inference times for LLMs is a multifaceted task that requires careful consideration of various factors. To achieve fast and responsive LLM serving, consider the following recommendations:

  1. Identify your optimization target: Determine whether you prioritize interactive performance, throughput, or cost efficiency. Understand the trade-offs associated with each target.
  2. Pay attention to latency components: Time-to-first-token and time-per-output-token are crucial metrics that influence user experience. Balance these to meet your application's requirements.
  3. Focus on memory bandwidth: Efficient memory bandwidth utilization is key for LLM inference. Optimize your system to make the most of available memory bandwidth.
  4. Implement batching: Batching multiple requests together is essential for achieving high throughput and efficiently utilizing GPUs. Choose the appropriate batching method based on your use case.
  5. Explore in-depth optimizations: Beyond standard optimization techniques, delve into deeper system-level optimizations, like quantization and KV cache management.
  6. Choose hardware configurations wisely: Select deployment hardware based on your model type and expected workload. Consider factors such as batch size, tensor parallelism, and memory bandwidth.
  7. Make data-driven decisions: Measure end-to-end server performance and adapt to real-world conditions. Be aware that differences in hardware between cloud providers can impact performance.


Deploying reliable and affordable AI solutions requires optimizing the inference time of your models. You can lower expenses, improve user experience, and increase system efficiency by implementing the above-discussed techniques. As with any optimization strategy, the best course of action depends on your unique use case and available resources. Your specific needs and priorities will determine which approach—model pruning, quantization, knowledge distillation, batch inference, or a mixed system of experts—is best. You can significantly reduce inference times and increase the effectiveness and economy of your LLM-powered applications by carefully choosing and putting these strategies into practice.


Research paper: Batch Prompting: Efficient Inference with Large Language Model APIs

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