Fast providers offering open source LLMs are breaking past previous speed limits, delivering low latency and strong performance that make them suitable for real time interaction, long running coding tasks, and production SaaS applications.
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Large language models became truly fast when Groq introduced its own custom processing architecture called the Groq Language Processing Unit LPU. These chips were designed specifically for language model inference and immediately changed expectations around speed. At the time, GPT-4 responses averaged around 25 tokens per second. Groq demonstrated speeds of over 150 tokens per second, showing that real-time AI interaction was finally possible.
This shift proved that faster inference was not only about using more GPUs. Better silicon design or optimized software could dramatically improve performance. Since then, many other companies have entered the space, pushing token generation speeds even further. Some providers now deliver thousands of tokens per second on open source models. These improvements are changing how people use large language models. Instead of waiting minutes for responses, developers can now build applications that feel instant and interactive.
In this article, we review the top five super fast LLM API providers that are shaping this new era. We focus on low latency, high throughput, and real-world performance across popular open source models.
Cerebras stands out for raw throughput by using a very different hardware approach. Instead of clusters of GPUs, Cerebras runs models on its Wafer-Scale Engine, which uses an entire silicon wafer as a single chip. This removes many communication bottlenecks and allows massive parallel computation with very high memory bandwidth. The result is extremely fast token generation while still keeping first-token latency low.
This architecture makes Cerebras a strong choice for workloads where tokens per second matter most, such as long summaries, extraction, and code generation, or high-QPS production endpoints.
Example performance highlights:
What to note: Cerebras is clearly speed-first. In some cases, such as GLM-4.7, pricing can be higher than slower providers, but for throughput-driven use cases, the performance gains can outweigh the cost.
Groq is known for how fast its responses feel in real use. Its strength is not only token throughput, but extremely low time to first token. This is achieved through Groq’s custom Language Processing Unit, which is designed for deterministic execution and avoids the scheduling overhead common in GPU systems. As a result, responses begin streaming almost immediately.
This makes Groq especially strong for interactive workloads where responsiveness matters as much as raw speed, such as chat applications, agents, copilots, and real-time systems.
Example performance highlights:
When it is a great pick: Groq excels in use cases where fast response startup is critical. Even when other providers offer higher peak throughput, Groq consistently delivers a more responsive and snappy user experience.
SambaNova delivers strong performance by using its custom Reconfigurable Dataflow Architecture, which is designed to run large models efficiently without relying on traditional GPU scheduling. This architecture streams data through the model in a predictable way, reducing overhead and improving sustained throughput. SambaNova pairs this hardware with a tightly integrated software stack that is optimized for large transformer models, especially the Llama family.
The result is high and stable token generation speed across large models, with competitive first token latency that works well for production workloads.
Example performance highlights:
When it is a great pick: SambaNova is a strong option for teams deploying Llama based models who want high throughput and reliable performance without optimizing purely for a single peak benchmark number.
Fireworks AI achieves high token speed by focusing on software first optimization rather than relying on a single hardware advantage. Its inference platform is built to efficiently serve large open source models by optimizing model loading, memory layout, and execution paths. Fireworks applies techniques such as quantization, caching, and model specific tuning so each model runs close to its optimal performance. It also uses advanced inference methods like speculative decoding to increase effective token throughput without increasing latency.
This approach allows Fireworks to deliver strong and consistent performance across multiple model families, making it a reliable choice for production systems that use more than one large model.
Example performance highlights:
When it is a great pick: Fireworks works well for teams that need strong and consistent speed across several large models, making it a solid all around choice for production workloads.
Baseten shows particularly strong results on GLM 4.7, where it performs close to the top tier of providers. Its platform focuses on optimized model serving, efficient GPU utilization, and careful tuning for specific model families. This allows Baseten to deliver solid throughput on GLM workloads, even if its performance on very large GPT OSS models is more moderate.
Baseten is a good option when GLM 4.7 speed is a priority rather than peak throughput across every model.
Example performance highlights:
When it is a great pick: Baseten deserves attention if GLM 4.7 performance matters most. In this dataset, it sits just behind Fireworks on that model and well ahead of many other providers, even if it does not compete at the very top on larger GPT OSS models.
The table below compares the providers based on token generation speed and time to first token across large language models, highlighting where each platform performs best.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
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