Nvidia Achieves AI Speed Milestone: Surpasses 1,000 Tokens Per Second
If there was ever an AI speed milestone after a very long time indeed, Nvidia has surely put that behind. Word around town, as validated by Artificialists, is that Nvidia has breached a significant AI speed milestone with the record of more than 1,000 tokens per second (TPS) for a single user: a record, my dear, made using Meta's bulky Llama 4 Maverick large language model, powered all by Nvidia's DGX B200 system with eight of their formidable Blackwell GPUs.
Leaving Competitors in the Dust
For context, Nvidia didn't just go an inch past the last record holder, SambaNova; they comfortably beat them by a 31% margin at an impressive 1,038 TPS per user, while SambaNova came at 792 TPS. As per the benchmarks, Nvidia and SambaNova are currently on their own in this performance race. Other heavyweights, like Amazon and Groq, are milling about the 300 TPS region, whereas quite a number are lagging below 200 TPS.
Strategy: More Than Just Raw Power
How did Nvidia succeed. It was not a matter of cranking good hardware to solve the problem, although the Blackwell GPUs were in there. The greater part of it was actually some brilliant software tricks. Nvidia engineers worked on intensive fine-tuning targeted explicitly on the Llama 4 Maverick model.
There are two remarkable optimizations that, alone, have delivered up to a hundred percent improvement:
- TensorRT-LLM: Nvidia's own SDK for optimizing large language model inference
- Speculative Decoding (Eagle-3 style): Picture a small fast-going "draft" AI working in parallel, and making rapid assumptions about the next few words (or tokens) that the larger AI is supposed to be generating. The larger, slower model then checks these guesses in parallel. Good guesses will save time for the big guys.
Here is a description of speculative decoding from Nvidia:
A common method for speeding up inference on LLMs while preserving the quality of generated text. It does so by getting a smaller, faster "draft" model to predict a sequence of speculative tokens, which a second, larger target LLM checks in parallel. The speedup is achieved by generating potentially several tokens in one iteration of the target model at the expense of the overhead of running the draft model.
But they didn't stop there; speed, accuracy, and optimization were improved with FP8 data types instead of the usual BF16, leveraging Attention mechanisms, and using the increasingly popular Mixture of Experts (MoE) AI technique. In addition, the software engineers delved deep into CUDA kernels and squeezed out even more performance improvements by fine-tuning aspects such as spatial partitioning and GEMM weight shuffling.
Why "Tokens Per Second Per User" Matters to You
Now, what's a token, and why should I care how fast a GPU can churn them out per user. Fine question. Tokens are the basic units for AI models, such as ChatGPT or Copilot. When you write a sentence, it gets chopped into these tokens. These input tokens will be processed by the AI, which generates the output tokens that form the basis for its answer.
Here, "per user" is what matters. This benchmark evaluates the experience of a single user interacting with an AI. The faster a system can churn through tokens for your specific request, the quicker you get a response. So this record is more than just a technical achievement; instead, it is a direct ticket toward a more responsive and amiable world of AI.
What Does That Mean For The Future Of AI
The latest Nvidia victory showcases its leading position in AI hardware and reflects the fierce race to accelerate "token-generation" speed. Such a metric has become prominent in the eyes of Jensen Huang, Nvidia's CEO, as a marker of AI progress. Such a breakthrough is a clear message that AI is getting faster, lighter, and in the end, more applicable to our daily lives.
