NVIDIA to Unveil Heterogeneous AI Inference Stack at GTC 2026, Integrating Groq for Ultra-Low-Latency Decode

NVIDIA is set to formalize a heterogeneous AI stack, integrating Groq's low-latency LPU technology with its own Rubin GPUs and Vera CPUs as a specialized product line.

Groq's unique SRAM-based, compiler-driven chip is ideal for the 'decode' phase of AI inference, providing the predictable, fast responses needed for interactive applications like chatbots.

This strategy does not mean SRAM will replace HBM; rather, it creates a tiered system where GPUs handle throughput, LPUs manage latency, and other components optimize the entire process for performance and cost.

NVIDIA is poised to unveil a groundbreaking new strategy for AI at its upcoming GTC 2026 conference.

The core of this strategy is a 'heterogeneous inference stack'. Think of it like assembling a specialized team where each member excels at a specific task. Instead of one chip doing everything, NVIDIA plans to use its powerful Rubin GPUs for massive data processing, a new Rubin CPX accelerator for understanding long and complex prompts, and now, technology from a company called Groq to deliver lightning-fast, token-by-token responses.

So, what makes Groq's technology special? It comes down to two key concepts: determinism and SRAM. First, Groq's chips, known as LPUs, use an ultra-fast on-chip memory called SRAM. This allows every single calculation to be scheduled with cycle-accurate precision, guaranteeing predictable, ultra-low latency. This is a fundamentally different approach from NVIDIA's GPUs, which are masterfully designed to achieve high throughput by cleverly 'hiding' the unpredictable delays of accessing larger HBM memory.

This difference is precisely why NVIDIA is pursuing product-line integration rather than trying to rebuild its GPUs. The Groq-style LPU will be a specialist, handling the 'decode' phase of AI inference—generating a response one word at a time. This is critical for a smooth user experience in chatbots, copilots, and other interactive services. Meanwhile, the GPUs will continue to dominate tasks that benefit from processing huge batches of data all at once.

But does this mean fast SRAM will replace the HBM memory paired with GPUs? Not at all. SRAM is significantly more expensive per bit and its density doesn't scale as easily. HBM will remain the cost-effective workhorse for handling large models and massive datasets. The future is a balanced, tiered system where SRAM offers a premium low-latency lane, while HBM provides the sheer bandwidth and capacity needed for large-scale AI.

This entire integrated system, orchestrated by NVIDIA's new Vera CPU and advanced networking, represents the next frontier in AI infrastructure. It's a sophisticated approach designed to deliver the best of all worlds: speed for interaction, power for complexity, and efficiency for massive-scale operations.

Heterogeneous Stack: A computing system that uses different types of processors (e.g., CPU, GPU, LPU) together to perform a task more efficiently than a single type of processor could.
SRAM vs. HBM: Two types of memory. SRAM is extremely fast but expensive and has low capacity, typically used directly on a chip. HBM (High Bandwidth Memory) is a type of DRAM that offers a balance of very high speed and larger capacity, used alongside high-performance processors like GPUs.
Inference Decode: A key stage in how an AI model generates a response. After processing the initial prompt (prefill), the model generates the output word by word (or token by token) in the decode stage, where low latency is critical for a real-time feel.

NVIDIA to Unveil Heterogeneous AI Inference Stack at GTC 2026, Integrating Groq for Ultra-Low-Latency Decode

NVIDIA is set to formalize a heterogeneous AI stack, integrating Groq's low-latency LPU technology with its own Rubin GPUs and Vera CPUs as a specialized product line.

Groq's unique SRAM-based, compiler-driven chip is ideal for the 'decode' phase of AI inference, providing the predictable, fast responses needed for interactive applications like chatbots.

NVIDIA is poised to unveil a groundbreaking new strategy for AI at its upcoming GTC 2026 conference.

Heterogeneous Stack: A computing system that uses different types of processors (e.g., CPU, GPU, LPU) together to perform a task more efficiently than a single type of processor could.
SRAM vs. HBM: Two types of memory. SRAM is extremely fast but expensive and has low capacity, typically used directly on a chip. HBM (High Bandwidth Memory) is a type of DRAM that offers a balance of very high speed and larger capacity, used alongside high-performance processors like GPUs.
Inference Decode: A key stage in how an AI model generates a response. After processing the initial prompt (prefill), the model generates the output word by word (or token by token) in the decode stage, where low latency is critical for a real-time feel.