Real-time LLM Inference on Standard GPUs: 3k tokens/s per request
The current limitation is that existing inference software stacks are not optimized for single-request decoding speed, which is a memory-bandwidth max
TL;DR
- Real-time LLM inference on standard GPUs can reach 3k tokens/s per request
- Optimizing the whole software stack with architecture/engine/kernel co-design is crucial for fast inference
- Standard datacenter GPU hardware has a higher decoding-speed ceiling than current inference stacks expose
- The limiting factor is existing inference software stacks not optimized for single-request decoding speed
The primary source article explains why optimizing for single-request LLM decoding speed is important for AI agents and how standard datacenter GPUs can achieve this speed. According to the article, the key to reaching this speed is co-designing the model architecture, runtime, and low-level GPU code as a single latency-optimized pipeline. This approach allows for extremely fast single-request decoding without the need for proprietary silicon.
What the data shows
The article highlights that existing inference software stacks are not optimized for single-request decoding speed, which is a memory-bandwidth maximization problem rather than a FLOPS one. The data shows that standard datacenter GPUs can achieve a much higher decoding-speed ceiling than current inference stacks expose, but this requires optimizing the whole software stack. The article also notes that inference benchmarks typically conflate three quantities: aggregate throughput, time to first token, and decode speed per request. Decode speed per request is the metric that matters for AI agents, as it governs every long serial interaction.
What this means for AI readers
For AI readers, the ability to achieve real-time LLM inference on standard GPUs means that they can build more responsive and interactive products. As the article notes, if an agent needs to generate 50,000 tokens in a workflow, 100 tokens/s is roughly eight minutes, while 3,000 tokens/s is under twenty seconds. This difference can significantly change the product that can be built. The article also explains that agentic software engineering is a sequential loop, and the generation-heavy steps set the loop rate. Therefore, faster decode speeds can lead to more efficient and effective AI agents.
What to do right now
The article invites readers to test the speed of their 2B coding model in their live coding playground: playground.kog.ai. This allows readers to experience the fast single-request decoding speed firsthand. The article also notes that the 2B coding model is small and not a frontier model, but it is still quite capable when fine-tuned for specific software engineering tasks.
Bottom line
The primary source article demonstrates that real-time LLM inference on standard GPUs is possible, achieving 3k tokens/s per request. This is made possible by optimizing the whole software stack with architecture/engine/kernel co-design. The article highlights the importance of decode speed per request for AI agents and how standard datacenter GPUs can achieve this speed without the need for proprietary silicon. By co-designing the model architecture, runtime, and low-level GPU code, developers can build more responsive and interactive products.
Frequently asked questions
Q: What is the current limitation of existing inference software stacks?
The current limitation is that existing inference software stacks are not optimized for single-request decoding speed, which is a memory-bandwidth maximization problem rather than a FLOPS one.
Q: What is the key to achieving fast single-request decoding speed?
The key is co-designing the model architecture, runtime, and low-level GPU code as a single latency-optimized pipeline.
Q: How can readers experience the fast single-request decoding speed firsthand?
Readers can test the speed of the 2B coding model in the live coding playground: playground.kog.ai.
Q: What is the significance of achieving 3k tokens/s per request for AI agents?
Achieving 3k tokens/s per request can significantly change the product that can be built, as it enables more responsive and interactive products, and faster decode speeds can lead to more efficient and effective AI agents.
