--- title: "How NVIDIA Groq 3 LPX Unlocks Ultrafast Interactivity at Long Context on NVIDIA Vera Rubin" url: https://daily.dev/posts/how-nvidia-groq-3-lpx-unlocks-ultrafast-interactivity-at-long-context-on-nvidia-vera-rubin-qfmsppyoi source_url: https://developer.nvidia.com/blog/how-nvidia-groq-3-lpx-unlocks-ultrafast-interactivity-at-long-context-on-nvidia-vera-rubin type: article source: "NVIDIA Developer" published: 2026-08-24T15:04:31.023Z updated: 2026-08-24T15:05:27.982Z tags: ["agentic-ai", "ai-inference"] reading_time: 10 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # How NVIDIA Groq 3 LPX Unlocks Ultrafast Interactivity at Long Context on NVIDIA Vera Rubin **[NVIDIA Developer](https://daily.dev/sources/nvidiadev)** · 10 min read · 0 upvotes · 0 comments ## Summary NVIDIA Groq 3 LPX is presented as the interactive inference accelerator paired with the Vera Rubin NVL72 platform, targeting ultrafast, long-context agentic AI serving. Third-party benchmarks from Artificial Analysis show Gemma 4 31B running at 3,431 output tokens/second at 100K context and 3,382 tokens/second at 10K context, with minimal latency variation across context lengths. A separate SPEED-Bench coding benchmark measured 4,767 median tokens/second, with 20% of tasks exceeding 5,500 tokens/second. The piece details how Groq 3 LPX's deterministic, compiler-scheduled execution and chip-to-chip networking minimize first-bit latency and enable fine-grained overlap of computation and communication, and outlines serving configurations combining LPX with Vera Rubin NVL72 such as prefill-decode disaggregation, attention-FFN disaggregation, and speculative decoding. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How fast does NVIDIA Groq 3 LPX run inference at 100K context length compared to 10K context? NVIDIA Groq 3 LPX generated 3,431 output tokens per second at 100K input context length running Gemma 4 31B, versus 3,382 tokens per second at 10K context length, according to Artificial Analysis benchmarks. This minimal variation stems from the LPU's deterministic architecture and high tensor parallelism, which keep latency stable regardless of context length. _daily.dev surfaces benchmark comparisons like this for engineers evaluating long-context inference hardware._ ### What makes tensor parallelism inefficient at small batch sizes for high-interactivity inference? At very small batch sizes required for high interactivity, the fixed coordination cost of tensor parallelism can outweigh its speedup benefits. Each data transfer between chips has a first-bit latency (link selection, synchronization, conflict arbitration) plus transfer time, and at small batch sizes this fixed latency becomes comparable to or larger than the compute time saved by splitting work across chips. _Engineers tuning inference systems track these architecture tradeoffs through daily.dev for tensor parallelism decisions._ ### How fast can Groq 3 LPX generate code compared to typical agentic coding tools? NVIDIA Groq 3 LPX produced coding answers at a median speed of 4,767 output tokens per second on the SPEED-Bench benchmark, with 20% of tasks completing above 5,500 tokens per second, running the Gemma 4 31B model. This compares to roughly 60 tokens per second for many popular agentic coding tools today, meaning a 5,000-token response takes about 1.5 seconds versus roughly 50 seconds at 100 tokens per second. _daily.dev helps developers evaluating agentic coding tool speed keep tabs on inference performance benchmarks._ ## Similar posts on daily.dev - [Inside NVIDIA Groq 3 LPX: The Low-Latency Inference Accelerator for the NVIDIA Vera Rubin Platform](https://daily.dev/posts/inside-nvidia-groq-3-lpx-the-low-latency-inference-accelerator-for-the-nvidia-vera-rubin-platform-6kpvreurx) · NVIDIA Developer · 1 upvotes · 0 comments - [How the NVIDIA Vera Rubin Platform is Solving Agentic AI’s Scale-Up Problem](https://daily.dev/posts/how-the-nvidia-vera-rubin-platform-is-solving-agentic-ai-s-scale-up-problem-iyn67thas) · NVIDIA Developer · 0 upvotes · 0 comments --- Tags: [#agentic-ai](https://daily.dev/tags/agentic-ai), [#ai-inference](https://daily.dev/tags/ai-inference) [View this post on daily.dev](https://daily.dev/posts/how-nvidia-groq-3-lpx-unlocks-ultrafast-interactivity-at-long-context-on-nvidia-vera-rubin-qfmsppyoi)