Top 5 Super Rapid LLM API Providers

# Entry

Enormous language models became really swift 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 speed expectations. At that time, the average GPT-4 response was approx 25 tokens per second. Groq showed a speed of over 150 tokens per second, showing that real-time AI interaction is finally possible.

This change proved that faster inference isn’t just about using more GPUs. Better silicon design or optimized software can dramatically improve performance. Since then, many other companies have entered this space, further increasing the speed of token generation. Some vendors now provide thousands of tokens per second in open source models. These improvements are changing the way people exploit immense language models. Instead of waiting minutes for responses, developers can now create applications that work instantly and interactively.

In this article, we review five of the best super-fast API LLM providers that are shaping this up-to-date era. We focus on low latency, high throughput, and real-world performance on popular open source models.

# 1. Brains

Brain excels in raw throughput by using a completely different hardware approach. Instead of clusters of GPUs, Cerebras runs models based on the Wafer-Scale Engine, which uses the entire silicon wafer as a single chip. This eliminates many communication bottlenecks and enables massive parallel computations with very high memory bandwidth. The result is extremely swift token generation with low first token latency.

This architecture makes Cerebras a good choice for workloads where tokens per second matter most, such as long summaries, code extraction and generation, or high-QPS production endpoints.

Example performance highlights:

• 3115 tokens per second on gpt-oss-120B (high) with first token ~0.28 s
• 2782 tokens per second on gpt-oss-120B (low) with first token ~0.29 s
• 1669 tokens per second in GLM-4.7 with first token ~0.24 s
• 2041 tokens per second on Lama 3.3 70B with first token ~0.31 s

What to look out for: Cerebras clearly focuses on speed. In some cases, such as GLM-4.7, prices may be higher than slower providers, but for throughput-based applications, the performance gains may outweigh the costs.

# 2. Grok

Grok is known for its responsiveness in real exploit. Its strength is not only token throughput, but also an extremely miniature time to reach the first token. This is achieved through a custom Groq language processing unit, which is designed for deterministic execution and avoids the scheduling overhead common in GPU systems. As a result, responses begin almost immediately.

This makes Groq particularly useful for interactive workloads where responsiveness is as crucial as sheer 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 first token ~0.17 s
• 914 tokens per second on gpt-oss-20B (low) with first token ~0.17 s
• 467 tokens per second on gpt-oss-120B (high) with first token ~0.17 s
• 463 tokens per second on gpt-oss-120B (low) with first token ~0.16 s
• 346 tokens per second on Lama 3.3 70B with first token ~0.19 s

When this is a great choice: Groq excels in exploit cases where swift startup response is critical. Even though other providers offer higher peak throughput, Groq consistently delivers a more responsive and faster user experience.

# 3. SambaNew

SambaNew delivers high performance by using a custom Reconfigurable Dataflow Architecture, which is designed to run immense models efficiently without relying on time-honored GPU scheduling. This architecture streams data through the model in a predictable manner, reducing overhead and improving consistent throughput. SambaNova combines this hardware with a tightly integrated software stack optimized for immense transformer models, especially the Llama family.

The result is high and stable token generation speed on immense models, with competitive first token latency that performs well for production workloads.

Example performance highlights:

• 689 tokens per second on Llama 4 Maverick with first token ~0.80 s
• 611 tokens per second on gpt-oss-120B (high) with first token ~0.46 s
• 608 tokens per second on gpt-oss-120B (low) with first token ~0.76 s
• 365 tokens per second on Lama 3.3 70B with first token ~0.44 s

When this is a great choice: SambaNova is a good option for teams implementing llama-based models who want high throughput and reliable performance without optimizing solely for a single peak benchmark.

# 4. AI fireworks

AI fireworks achieves high token speed by focusing on software optimization rather than a single hardware advantage. Its inference framework is built to efficiently handle immense open source models by optimizing model loading, memory layout, and execution paths. Fireworks uses techniques such as quantization, caching, and model-specific tuning to ensure that each model performs close to its optimal performance. It also uses advanced inference methods such as speculative decoding to escalate the effective throughput of the token without increasing latency.

This approach allows Fireworks to deliver high and consistent performance across multiple model families, making it a reliable choice for production systems that exploit more than one immense model.

Example performance highlights:

• 851 tokens per second on gpt-oss-120B (low) with first token ~0.30 s
• 791 tokens per second on gpt-oss-120B (high) with first token ~0.30 s
• 422 tokens per second on GLM-4.7 with first token ~0.47 s
• 359 tokens per second with no reasoning GLM-4.7 with first token ~0.45 s

When this is a great choice: Fireworks works well for teams that need swift and consistent speeds across several immense models, making it a solid all-around choice for production workloads.

# 5. Basset Hound

Basset hound It shows particularly good results in the GLM 4.7 version, where it achieves results similar to those of top-shelf providers. Its platform focuses on optimized model sharing, competent GPU utilization, and fine-tuning for specific model families. This allows Baseten to deliver solid throughput for GLM workloads, even if its performance on very immense GPT OSS models is more moderate.

Baseten is a good option when the priority is the speed of the GLM 4.7 rather than the maximum throughput on each model.

Example performance highlights:

• 385 tokens per second in GLM 4.7 with first token ~0.59 s
• 369 tokens per second in GLM 4.7 without reasoning with first token ~0.69 s
• 242 tokens per second on gpt-oss-120B (high)
• 246 tokens per second on gpt-oss-120B (low)

When this is a great choice: Baseten deserves your attention if the performance of the GLM 4.7 is most crucial. In this dataset, it ranks just behind Fireworks on this model and well ahead of many other vendors, even if it doesn’t compete at the very top with the larger GPT OSS models.

# Comparison of superfast LLM API providers

The table below compares providers based on token generation speed and time to first token appearance in immense language models, highlighting where each platform performs best.

Abid Ali Awan (@1abidaliawan) is a certified data science professional who loves building machine learning models. Currently, he focuses on creating content and writing technical blogs about machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunications Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.