Competition in Cheap and Fast LLM Token Generation 🚀

The field of large language model (LLM) token generation is rapidly advancing, with several companies competing to offer the fastest, most affordable, and efficient solutions. In this post, we'll explore the innovations from Groq, SambaNova, Cerebras, and Together.ai, highlighting their unique approaches and technologies. This will give you a comprehensive view of the current landscape and how these companies are shaping the future of AI inference.

1. Groq: Speed and Efficiency Redefined ⚡

Groq is revolutionizing AI inference with its LPU™ AI technology. The LPU is designed to deliver exceptional speed and efficiency, making it a leading choice for fast and affordable AI solutions. Here's what sets Groq apart:

• Speed: Groq’s LPUs provide high throughput and low latency, ideal for applications that demand rapid processing.
• Affordability: By eliminating the need for external switches, Groq reduces CAPEX for on-prem deployments, offering a cost-effective solution.
• Energy Efficiency: Groq’s architecture is up to 10X more energy efficient compared to traditional systems, which is crucial as energy costs rise.

Discover more about Groq’s offerings at Groq.

2. SambaNova: Enterprise-Grade AI at Scale 🏢

SambaNova’s fourth-generation SN40L chip is making waves with its dataflow architecture, designed for handling large models and complex workflows. Key features include:

• Performance: The SN40L chip delivers world record performance with Llama 3.1 405b, utilizing a three-tier memory architecture to manage extensive models efficiently.
• Dataflow Architecture: This architecture optimizes communication between computations, resulting in higher throughput and lower latency.
• Ease of Use: SambaNova’s software stack simplifies the deployment and management of AI models, providing a comprehensive solution for enterprises.

Learn more about SambaNova’s technology at SambaNova.

3. Cerebras: The Fastest Inference Platform ⏱️

Cerebras is known for its Wafer-Scale architecture and weight streaming technology, offering some of the fastest inference speeds available. Highlights include:

• Inference Speed: Cerebras claims their platform is 20X faster than GPUs, providing a significant boost in performance.
• Context Length: Their technology supports a native context length of 50K tokens, which is essential for analyzing extensive documents.
• Training Efficiency: With support for dynamic sparsity, Cerebras models can be trained up to 8X faster than traditional methods.

Explore Cerebras’ capabilities at Cerebras.

4. Together.ai: Cost-Effective and Scalable Inference 💸

Together.ai stands out with its cost-efficient inference solutions and scalable architecture. Key points include:

• Cost Efficiency: Their platform is up to 11X cheaper than GPT-4o when using models like Llama-3, offering significant savings.
• Scalability: Together.ai automatically scales capacity to meet demand, ensuring reliable performance as applications grow.
• Serverless Endpoints: They offer access to over 100 models through serverless endpoints, including high-performance embeddings models.

Find out more about Together.ai at Together.ai.

Integrating Insights with Murat Karakaya Akademi 🎥

The advancements by Groq, SambaNova, Cerebras, and Together.ai highlight the rapid evolution in AI inference technologies. On my YouTube channel, "Murat Karakaya Akademi," I frequently discuss such innovations and their impact on the AI landscape. Recently, viewers have been curious about how these technologies compare and what they mean for future AI applications.

For in-depth discussions and updates on the latest in AI, visit Murat Karakaya Akademi. Don't forget to subscribe for the latest insights and analysis!

Sources 📚

[1] Groq: https://groq.com/
[2] SambaNova: https://sambanova.ai/
[3] Cerebras: https://cerebras.ai/
[4] Together.ai: https://www.together.ai/

 🚀 LLM API Rate Limits & Robust Applications Development 🚀

When building robust applications with Large Language Models (LLMs), one of the key challenges is managing API rate limits. These limits, like requests per minute (RPM) and tokens per minute (TPM), are crucial for ensuring fair use but can become a bottleneck if not handled properly.

💡 For instance, the Gemini API has specific rate limits depending on the model you choose. For the gemini-1.5-pro, the free tier allows only 2 RPM and 32,000 TPM, while the pay-as-you-go option significantly increases these limits to 360 RPM and 4 million TPM. You can see the full breakdown here [1].

The LLM providers, like OpenAI and Google, impose these limits to prevent abuse and ensure efficient use of their resources. For example, OpenAI's guidance on handling rate limits includes tips on waiting until your limit resets, sending fewer tokens, or implementing exponential backoff [2]. However, this doesn’t mean you’re left in the lurch. For instance, Google’s Gemini API offers a form to request a rate limit increase if your project requires it [3].

🔍 Handling Rate Limits Effectively:

• 💡 Automatic Retries: When your requests fail due to transient errors, implementing automatic retries can help keep your application running smoothly.
• 💡 Manual Backoff and Retry: For more control, consider a manual approach to managing retries and backoff times. Check out how this can be done with Gemini API [4].

At Murat Karakaya Akademi (https://lnkd.in/dEHBv_S3), I often receive questions about these challenges. Developers are curious about how to effectively manage rate limits and ensure their applications are resilient. In one of my recent tutorials, I discussed these very issues and provided strategies to overcome them.

💡 Interested in learning more? Visit my YouTube channel, subscribe, and join the conversation! 📺

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#APIRateLimits #LLM #GeminiAPI #OpenAI #MuratKarakayaAkademi

[1] Full API rate limit details for Gemini-1.5-pro: https://lnkd.in/dQgXGQcm
[2] OpenAI's RateLimitError and handling tips: https://lnkd.in/dx56CE9z
[3] Request a rate limit increase for Gemini API: https://lnkd.in/dn3A389g
[4] Error handling strategies in LLM APIs: https://lnkd.in/dt7mxW46

🚀 What is an LLM Inference Engine?

I've recently received questions about LLM inference engines on my YouTube channel, "Murat Karakaya Akademi." This topic is becoming increasingly important as more organizations integrate Large Language Models (LLMs) into their operations. If you're curious to learn more or see a demonstration, feel free to visit my channel (https://www.youtube.com/@MuratKarakayaAkademi).

🚀 What is an LLM Inference Engine?

An LLM inference engine is a powerful tool designed to make serving LLMs faster and more efficient. These engines are optimized to handle high throughput and low latency, ensuring that LLMs can respond quickly to a large number of requests. They come with advanced features like response streaming, dynamic request batching, and support for multi-node/multi-GPU serving, making them essential for production environments.

Why Use Them?

• 🎯 Simple Launching: Easily serve popular LLMs with a straightforward setup [1].
• 🛡️ Production Ready: Equipped with distributed tracing, Prometheus metrics, and Open Telemetry [2].
• ⚡ Performance Boost: Leverage Tensor Parallelism, optimized transformers code, and quantization techniques to accelerate inference on multiple GPUs [3].
• 🌐 Broad Support: Compatible with NVIDIA GPUs, AMD and Intel CPUs, TPUs, and more [1].

Examples include:

• vLLM: Known for its state-of-the-art serving throughput and efficient memory management [1].
• Ray Serve: Excellent for model composition and low-cost serving of multiple ML models [2].
• Hugging Face TGI: A toolkit for deploying and serving popular open-source LLMs [3].

#LLM #MachineLearning #AI #InferenceEngine #MuratKarakayaAkademi

References: [1] What is vLLM? https://github.com/vllm-project/vllm
[2] Ray Serve Overview https://docs.ray.io/en/latest/serve/index.html?_gl=1*14i4ooq*_gcl_au*MTE0Mjg5OTE0Ni4xNzI0NjY5MTkx

[3] Hugging Face Text Generation Inference https://huggingface.co/docs/text-generation-inference/en/index 

Part G: Text Classification with a Recurrent Layer

 

Part G: Text Classification with a Recurrent Layer

Author: Murat Karakaya
Date created….. 17 02 2023
Date published… 08 04 2023
Last modified…. 08 04 2023

Description: This is the Part G of the tutorial series “Multi-Topic Text Classification with Various Deep Learning Models” which covers all the phases of multi-class  text classification:

• Exploratory Data Analysis (EDA),
• Text preprocessing
• TF Data Pipeline
• Keras TextVectorization preprocessing layer
• Multi-class (multi-topic) text classification
• Deep Learning model design & end-to-end model implementation
• Performance evaluation & metrics
• Generating classification report
• Hyper-parameter tuning
• etc.

We will design various Deep Learning models by using

• Keras Embedding layer,
• Convolutional (Conv1D) layer,
• Recurrent (LSTM) layer,
• Transformer Encoder block, and
• Pre-trained transformer (BERT).

We will cover all the topics related to solving Multi-Class Text Classification problems with sample implementations in Python / TensorFlow / Keras environment.

We will use a Kaggle Dataset in which there are 32 topics and more than 400K total reviews.

If you would like to learn more about Deep Learning with practical coding examples,

• Please subscribe to the Murat Karakaya Akademi YouTube Channel or
• Follow my blog on muratkarakaya.net
• Do not forget to turn on notifications so that you will be notified when new parts are uploaded.

You can access all the codes, videos, and posts of this tutorial series from the links below.

Accessible on:

• YouTube in English
• YouTube in Turkish
• muratkarakaya.net
• Github pages
• Github Repo
• Google Colab

PARTS

In this tutorial series, there are several parts to cover Text Classification with various Deep Learning Models topics. You can access all the parts from this index page.

In this part, we will use the Keras Bidirectional LSTM layer in a Feed Forward Network (FFN).

If you are not familiar with the Keras LSTM layer or the Recurrent Networks concept, you can check in the following Murat Karakaya Akademi YouTube playlists:

English:

• All About LSTM

Turkish

• LSTM hakkında herşey!

If you are not familiar with the classification with Deep Learning topic, you can find the 5-part tutorials in the below Murat Karakaya Akademi YouTube playlists:

• How to solve Classification Problems in Deep Learning with Tensorflow & Keras
• Derin Öğrenmede Sınıflandırma Çeşitleri Nelerdir? Keras ve TensorFlow ile Nasıl Yapılmalı? (in Turkish)

Photo by Markus Spiske on Unsplash

Part F: Text Classification with a Convolutional (Conv1D) Layer in a Feed-Forward Network

 

Part F: Text Classification with a Convolutional (Conv1D) Layer in a Feed-Forward Network

Author: Murat Karakaya
Date created….. 17 09 2021
Date published… 11 03 2022
Last modified…. 29 12 2022

Description: This is the Part F of the tutorial series “Multi-Topic Text Classification with Various Deep Learning Models” which covers all the phases of multi-class  text classification:

• Exploratory Data Analysis (EDA),
• Text preprocessing
• TF Data Pipeline
• Keras TextVectorization preprocessing layer
• Multi-class (multi-topic) text classification
• Deep Learning model design & end-to-end model implementation
• Performance evaluation & metrics
• Generating classification report
• Hyper-parameter tuning
• etc.

We will design various Deep Learning models by using

• Keras Embedding layer,
• Convolutional (Conv1D) layer,
• Recurrent (LSTM) layer,
• Transformer Encoder block, and
• Pre-trained transformer (BERT).

We will cover all the topics related to solving Multi-Class Text Classification problems with sample implementations in Python / TensorFlow / Keras environment.

We will use a Kaggle Dataset in which there are 32 topics and more than 400K total reviews.

If you would like to learn more about Deep Learning with practical coding examples,

• Please subscribe to the Murat Karakaya Akademi YouTube Channel or
• Follow my blog on muratkarakaya.net
• Do not forget to turn on notifications so that you will be notified when new parts are uploaded.

You can access all the codes, videos, and posts of this tutorial series from the links below.

Accessible on:

• YouTube in English
• YouTube in Turkish
• muratkarakaya.net
• Github pages
• Github Repo
• Google Colab

PARTS

In this tutorial series, there are several parts to cover Text Classification with various Deep Learning Models topics. You can access all the parts from this index page.

Photo by Josh Eckstein on Unsplash

Sequence To Sequence Learning With Tensorflow & Keras Tutorial Series

The Seq2Seq Learning Tutorial Series aims to build an Encoder-Decoder Model with Attention. I would like to develop a solution by showing the shortcomings of other possible approaches as well. Therefore, in the first 2 parts, we will observe that initial models have their own weaknesses. We will also understand why the Encoder-Decoder paradigm is so successful. 

You can access all the parts in the below links.

Photo by Clay Banks on Unsplash

 

Seq2Seq Learning Part A: Introduction & A Sample Solution with MLP Network

If you are interested in Seq2Seq Learning, I have good news for you. Recently, I have been working on Seq2Seq Learning and I decided to prepare a series of tutorials about Seq2Seq Learning from a simple Multi-Layer Perceptron Neural Network model to an Encoder-Decoder Model with Attention.

You can access all my SEQ2SEQ Learning videos on Murat Karakaya Akademi Youtube channel in ENGLISH or in TURKISH

You can access all the tutorials in this series from my blog on www.muratkarakaya.net

Thank you!

Photo by Hal Gatewood on Unsplash

 

SEQ2SEQ LEARNING Part B: Using the LSTM layer in a Recurrent Neural Network

Welcome to the Part B of the Seq2Seq Learning Tutorial Series. In this tutorial, we will use several Recurrent Neural Network models to solve the sample Seq2Seq problem introduced in Part A.

We will use LSTM as the Recurrent Neural Network layer in Keras.

You can access all my SEQ2SEQ Learning videos on Murat Karakaya Akademi Youtube channel in ENGLISH or in TURKISH

You can access all the tutorials in this series from my blog at www.muratkarakaya.net

If you would like to follow up on Deep Learning tutorials, please subscribe to my YouTube Channel or follow my blog on muratkarakaya.net. Thank you!

Photo by Jess Bailey on Unsplash

 

Seq2Seq Learning Part C: Basic Encoder-Decoder Architecture & Design

Welcome to the Part C of the Seq2Seq Learning Tutorial Series. In this tutorial, we will design a Basic Encoder-Decoder model to solve the sample Seq2Seq problem introduced in Part A.

We will use LSTM as the Recurrent Neural Network layer in Keras.

You can access all my SEQ2SEQ Learning videos on Murat Karakaya Akademi Youtube channel in ENGLISH or in TURKISH

You can access all the tutorials in this series from my blog at www.muratkarakaya.net

If you would like to follow up on Deep Learning tutorials, please subscribe to my YouTube Channel or follow my blog on muratkarakaya.net. Thank you!

Photo by Med Badr Chemmaoui on Unsplash