Large language model inference became dramatically faster when Groq introduced a custom chip built specifically for the job — its Language Processing Unit (LPU). Purpose-built silicon for language-model inference reset expectations around speed: where typical GPT-4-class responses once streamed at roughly a couple dozen tokens per second, LPU-based inference pushed well past a hundred tokens per second, making genuinely real-time AI interaction practical. That shift showed faster inference was not only about adding more GPUs; better chip design and optimized software can improve performance substantially. Since then several providers have entered the field, and some now stream hundreds to well over a thousand tokens per second on open-weight models. The five below are among the fastest LLM API providers available, based on independent inference benchmarks.
1. Cerebras
Cerebras runs inference on its Wafer-Scale Engine, an unusually large chip that keeps more of a model on a single piece of silicon. It is clearly speed-first: third-party benchmarks have shown it among the highest-throughput options on open-weight models such as the gpt-oss family, in some cases reaching into the thousands of tokens per second. For certain models the per-token price can be higher than slower providers, but for throughput-driven workloads the performance can outweigh the cost.
2. Groq
Groq is known for how responsive it feels in practice. Its strength is not only raw token throughput but also a very short time to first token, achieved through its custom LPU, which is designed for deterministic execution and avoids the scheduling overhead common in GPU systems. Responses begin streaming almost immediately, which makes Groq well suited to interactive workloads — chat applications, agents, copilots and other real-time systems — where responsiveness matters as much as peak speed.
3. SambaNova
SambaNova uses its own reconfigurable dataflow hardware to serve large open-weight models at high speed. It publishes competitive throughput figures on current open models and, like the other custom-silicon providers, aims to combine strong tokens-per-second numbers with low latency. Its trade-off, shared with other specialized-hardware providers, tends to be a narrower model catalog than general-purpose GPU platforms.
4. Fireworks AI
Fireworks AI leads on a software-first approach rather than a single hardware advantage. Its platform serves large open-source models efficiently by optimizing model loading, memory layout and execution paths, and it applies techniques such as quantization, caching and model-specific tuning so each model runs close to its optimal performance. It also uses advanced methods like speculative decoding to raise effective throughput without increasing latency. The result is consistent performance across multiple model families, which makes it a reliable choice for production systems that run more than one large model.
5. Baseten
Baseten focuses on optimized model serving, efficient GPU utilization and careful tuning for specific model families. In independent benchmarks it performs particularly well on GLM-class models, sitting close to the top tier there even though it is more moderate on the very largest gpt-oss models. It is a strong option when speed on a specific model such as GLM-4.7 matters more than peak throughput across every model.
Comparison of Fast LLM API Providers
The table below compares these providers on token-generation speed and time to first token across several large language models, showing where each platform performs best. The figures represent a specific benchmark snapshot and will change as providers and models are updated.
| provider | main power | Peak Throughput (TPS) | time of first token | best use case |
|---|---|---|---|---|
| Cerebras | Excessive throughput on very large models | Up to 3,115 TPS (GPT-OSS-120B) | ~0.24–0.31 sec | High-QPS endpoints, long generation, throughput-driven workloads |
| Groq | Fastest Feeling Reactions | Up to 935 TPS (gpt-oss-20B) | ~0.16–0.19 | Interactive Chat, Agent, Co-Pilot, Real-Time System |
| SambaNova | High Throughput for Llama Family Model | Up to 689 TPS (Llama 4 Maverick) | ~0.44–0.80 seconds | Llama-family deployment with stable, high throughput |
| Fireworks AI | Constant speed in larger models | Up to 851 TPS (GPT-OSS-120B) | ~0.30–0.47 seconds | Teams running multiple model families in production |
| Baseten | Strong GLM-4.7 performance | Up to 385 TPS (GLM-4.7) | ~0.59–0.69 seconds | GLM-centric deployment |
Limitations and What to Watch
Inference-speed rankings are moving targets. The tokens-per-second and time-to-first-token figures in benchmarks like this reflect specific models, prompt sizes, regions and dates, and they shift frequently as providers upgrade hardware and software — so current numbers should be checked against a live source such as independent inference benchmarks before making a decision. Raw speed is also only one factor: price per token, model selection, context-window limits, rate limits, reliability and data-handling terms often matter as much as throughput for production use. Custom-silicon providers tend to offer the highest speeds but a narrower model catalog, while GPU-based providers trade some peak speed for broader model coverage. Finally, faster generation does not by itself make an application better; how much context a model actually needs is a separate question, explored in this comparison of selective retrieval versus loading everything into context.
