Top 5 Super Fast LLM API Providers

# Introduction

Large language models got really fast when Grok introduced its own custom processing architecture called the Grok 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 were average 25 tokens per second. Grok demonstrated over speed 150 tokens per second, This shows that AI interactions in real time are finally possible.

This change proved that faster inference was not just about using more GPUs. Better silicon design or optimized software can dramatically improve performance. Since then, many other companies have entered the field, increasing the pace of token production even further. Some providers now distribute thousands of tokens per second on an open source model. These improvements are changing the way people use large language models. Instead of waiting minutes for responses, developers can now create 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.

# 1. Cerebrus

cerebrus Stands for raw throughput 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 overcomes many communication barriers and allows massively parallel computation with very high memory bandwidth. The result is that tokens are generated much faster 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:

• 3,115 tokens per second on GPT-OSS-120b (high), with ~0.28 sec first token
• 2,782 tokens per second on GPT-OSS-120B (low), with ~0.29 sec first token
• 1,669 tokens per second on GLM-4.7 with ~0.24s first token
• Llama 3.3 2,041 tokens per second on 70b with tokens ~0.31 sec earlier

What to note: Cerebras is clearly speed-first. In some cases, such as GLM-4.7, pricing may be higher than slower providers, but in throughput-driven use cases, the performance benefits may outweigh the costs.

# 2. Grok

grok It is known for how fast its responses are in actual use. Its strength is not only token throughput, but also extremely short time to first token. This is achieved through Grok’s custom language processing unit, which is designed for deterministic execution and avoids the scheduling overhead common in GPU systems. As a result, reactions start streaming almost instantly.

This makes Grok particularly robust for interactive workloads where responsiveness matters as much as raw speed, such as chat applications, agents, co-pilots, and real-time systems.

Example performance highlights:

• 935 tokens per second on gpt-oss-20B (high) with ~0.17s first token
• 914 tokens per second on GPT-OSS-20B (low) with tokens ~0.17 seconds earlier
• 467 tokens per second on gpt-oss-120b (high) with ~0.17s first token
• 463 tokens per second on GPT-OSS-120B (low) with ~0.16 sec first token
• Llama 3.3 346 tokens per second on 70b, with ~0.19 sec first token

When it’s a great choice: Grok excels in use cases where fast response startup is important. Even when other providers offer higher peak throughput, Grok consistently delivers a more responsive and faster user experience.

# 3. Sambanova

sambanova Provides strong performance using its custom reconfigurable dataflow architecture, designed to efficiently run large models without relying on traditional GPU scheduling. This architecture streams data through the model in a predictable manner, reducing overhead and continuously improving throughput. Sambanova combines this hardware with a tightly integrated software stack that is optimized for larger Transformer models, particularly the Llama family.

The result is high and stable token generation speeds in large models, with competitive first token latency that works well for production workloads.

Example performance highlights:

• 689 tokens per second on Llama 4 Maverick with ~0.80 seconds first token
• 611 tokens per second on GPT-OSS-120B (High) with ~0.46 sec first token
• 608 tokens per second on GPT-OSS-120B (low) with ~0.76 sec first token
• Llama 3.3 365 tokens per second on 70b with ~0.44 sec first token

When it’s a great choice: Sambanova is a strong choice for teams deploying Llama-based models that want high throughput and reliable performance without having to fully optimize for a single peak benchmark number.

# 4. Fireworks AI

fireworks ai Higher token speeds are achieved 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 that each model runs close to its optimal performance. It also uses advanced inference methods such as speculative decoding to increase effective token throughput without increasing latency.

This approach allows Fireworks to deliver robust and consistent performance across multiple model families, making it a reliable choice for production systems using more than one large model.

Example performance highlights:

• 851 tokens per second on gpt-oss-120b (low) with ~0.30 sec first token
• 791 tokens per second on gpt-oss-120B (high) with ~0.30 sec first token
• 422 tokens per second on GLM-4.7 with ~0.47s first token
• GLM-4.7 non renormalization at 359 tokens per second with ~0.45 sec first token

When it’s a great choice: Fireworks works well for teams that need strong and consistent speed across many large models, making it a solid choice for production workloads.

# 5. Besten

besten GLM shows particularly strong results at 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 Basetain to provide solid throughput on GLM workloads, even if its performance is more moderate on much larger GPT OSS models.

Basetain is a good choice when GLM 4.7 speed is a priority rather than peak throughput in every model.

Example performance highlights:

• 385 tokens per second on GLM 4.7 with ~0.59s first token
• GLM 4.7 non reson at 369 tokens per second with ~0.69 sec first token
• 242 tokens per second (high) on GPT-OSS-120B
• 246 tokens per second (low) on GPT-OSS-120B

When it’s a great choice: If GLM 4.7 performance matters most then Besten is worth considering. In this dataset, it is well behind Fireworks and ahead of many other providers on that model, even though it does not compete at the top on the larger GPT OSS model.

# Comparison of Super Fast LLM API Providers

The table below compares providers based on token creation speed and time to first token across large language models, showing where each platform performs best.

abid ali awan (@1Abidaliyawan) 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 telecommunications engineering. Their vision is to create AI products using graph neural networks for students struggling with mental illness.