We’ve crossed a threshold in AI performance. SambaNova Systems has demonstrated an impressive 2.4 tokens per millisecond with the Llama 3.2 1B model. That’s 2,400 tokens per second—a new benchmark that changes how we evaluate AI inference speeds.
The Millisecond Milestone: A New Era for AI Speed
We’ve been measuring AI inference speeds in tokens per second for quite some time. Now, SambaNova has driven performance to a point where tokens per millisecond (t/ms) becomes the relevant metric. This isn’t just a change in units; it signifies a sizable improvement in processing power that promises to reshape AI applications.
SambaNova’s SN40L chips, incorporating SRAM, HBM, and DDR in a three-tier memory system, are what makes this possible. This architecture allows smaller models like Llama 3.2 1B to reach speeds of approximately 2,381.82 tokens per second—or 2.4 tokens per millisecond.
| Measurement Unit | Value | Equivalent |
|---|---|---|
| Tokens per Millisecond | 2.4 | 2,400 tokens/second |
| Tokens per Second | 2,381.82 | 142,909.2 tokens/minute |
| Kilotokens per Second | 2.38 | 2,380 tokens/second |
The Significance of Token Speed in AI
Token processing speed is vital for AI performance, especially in applications requiring real-time interaction. Faster token speeds result in:
- Quicker responses during user interactions
- Increased efficiency for processing large batch jobs
- More effective use of hardware resources
- Better cost management for scalable AI deployments
These enhancements are essential for enterprise adoption, where mere milliseconds saved can impact operational costs and revenue streams.
SambaNova’s Hardware-First Approach
SambaNova’s success is rooted in its hardware-first design philosophy. Unlike general-purpose GPUs adapted for AI, SambaNova built its chips specifically for neural network operations from the outset.
The SN40L chips incorporate a data-flow architecture that reduces data movement, a common bottleneck in AI inference. By keeping data close to processing units and refining memory hierarchies, SambaNova attains throughput levels difficult for traditional architectures to match.
Performance Variations Across Models
Although the 2.4 tokens per millisecond with Llama 3.2 1B is notable, SambaNova’s performance with different models varies. The larger DeepSeek R1 671B model, for example, achieves 198 tokens per second on their platform. This emphasizes the inverse relationship between model size and processing speed.
This performance difference highlights the trade-offs in AI system design. Larger models typically offer higher capabilities, but at the cost of processing speed. SambaNova’s architecture handles this trade-off effectively, maintaining reasonable speeds even with sizable models.
Real-World Applications
What are the practical implications for real-world applications? Millisecond-level token processing transforms capabilities in several key areas:
- Improve Conversational AI by enabling near-instantaneous response times
- Faster Document Processing capable of handling extensive texts in record time
- Real-Time Media Analysis with minimal delays
- Enhanced Financial Systems for making quick decisions using up-to-the-minute text analysis
These advancements pave the way for new AI applications that were previously impractical due to speed constraints. This opens entirely new areas and markets where AI can offer value that was not possible before. As speed increases, what this means is that the scope for AI is increasing at a rate that mirrors it. The amount of real-time processing that is able to happen across all facets of life are expanding exponentially, providing that value every step of the way.
The Competitive Arena: Specialized vs General Purpose
SambaNova is not the only company improving AI inference speeds. Other chip manufacturers, such as DeepSeek, are also pushing performance limits. Their inference stack offers strong efficiency, achieving high-profit margins when compared to other AI infrastructure providers.
What makes the current environment compelling is the architectural variety. Some companies prefer general-purpose accelerators, while others like SambaNova are dedicated to more customized solutions. This strategy fosters innovation along different dimensions of performance, providing different companies using different tools to reach for higher peaks that become standards for the industry as a whole.
Economic Effects of Speed
These improvements in speed are reshaping the economics of AI inference. As I discussed in my analysis of AI intelligence costs dropping, faster speeds directly lower operating costs for AI systems. These reductions are not minor. They are major savings for any company that is going to want to make use of AI.
For instance, a chip processing 2.4 tokens per millisecond, compared to one operating at 0.5 tokens per millisecond, can:
- Serve nearly 5x more requests
- Reduce data center needs
- Lower power usage per token
- Improve the total cost of AI ownership
These savings are likely to encourage wider AI use across industries that have previously hesitated because of financial constraints since a reduction in constraints always allows easier growth for companies since it does provide better freedom.
Tailoring Models to Speed
While the numbers highlight Llama 3.2 1B’s performance, model selection matters. As I pointed out in my article on Claude as a specialized LLM, the best model depends on the application. Companies should not go for what is new just to do so, but it should be a practical application in some way that is easy to maintain and use.
SambaNova’s system appears optimized for smaller models like Llama 3.2 1B. This close integration of hardware and software capabilities is key to unlocking maximum performance. Future hardware designs are likely to focus even more on specific model needs. If companies do not make sure what they make is able to work with existing system, what will happen is a lot of time will be wasted getting hardware and software to work together, slowing down the whole company.
Future Outlook
The 2.4 tokens per millisecond achievement isn’t just a one-time speed record; it points to future trends. Several implications to consider:
- Real-Time, Multi-Modal Processing will see broad scaling
- Models on the Edge with more advanced features will become achievable
- Complex Architectures blending specialized AI chips with CPUs/GPUs will grow
- Application Patterns that take advantage of near-instant inference will take form
These advancements not only set new standards for what is needed in the industry but it pushes every other company to start doing what they can to follow the leader, which improves life and opportunity across the playing field. It leads to better products for the public and better conditions for companies.
Final Thoughts on AI Performance
SambaNova’s achievement highlights a paradigm shift in AI. Token generation measured in milliseconds signals an opportunity. Kilotoken-per-second speeds represent a new benchmark for top-tier systems that many expect to see for a while. However, as they continue pushing the limits in an attempt to not only improve their own products but to improve the whole field, opportunity continues to be made and soon more advanced chips and processes will be commonplace for other companies, and kilotoken will be an inferior and slow speed.
These speed breakthroughs will influence how we develop and deploy AI systems. Specialized architectures promise a new class of AI applications that meet needs once considered out of reach.
The goal for faster inference is always practical value. What SambaNova is doing is not just about one company breaking ground; it’s about opening up a door for all businesses to do the same. If the price of computing and running AI is more efficient and cheaper, what ends up happening is everyone gets to take advantage of this efficiency and can start using products to get more value out of them.
