Editorial illustration for NVIDIA Co-Design Boosts Sarvam AI Inference, Cuts TTFT Below One Second
NVIDIA Supercharges Sarvam AI's Sovereign LLM Engine
NVIDIA Co-Design Boosts Sarvam AI Inference, Cuts TTFT Below One Second
The race to sub-second time-to-first-token is a brutal one. For Sarvam AI’s sovereign models, the baseline was functional but far from fast enough. Profiling laid the truth bare: at high concurrency, the MoE architecture was a bottleneck, not a breakthrough.
The fix demanded more than a tweak, it called for a full hardware-software co-design. NVIDIA’s Nsight Systems traced every prefill and decode phase at 32 concurrent requests, revealing where latency hid. The result?
A kernel-and-precision strategy that slashed TTFT below one second, even as concurrency climbed.
While this configuration provided a robust functional baseline, profiling revealed that satisfying the sub-second TTFT at high concurrency required deeper optimization - leading us to the specific kernel and precision strategies detailed below. From profiling to performance: eliminating MoE bottlenecks Simulation data indicated that a concurrency range of 32 to 64 requests would offer the best chance of meeting SLA requirements. To identify the precise bottlenecks limiting token throughput in this concurrency range, the NVIDIA and Sarvam AI teams utilized NVIDIA Nsight Systems to capture execution traces of both the prefill and decode phases at a concurrency of 32 requests.
Out of the profiling trenches emerged a blueprint for speed. The sub-second time-to-first-token wasn’t a theoretical ceiling; it became a hard-won reality. By marrying NVIDIA’s deepest kernel expertise with Sarvam AI’s model architecture, the teams turned a concurrency bottleneck into a throughput asset.
The MoE pathways that once choked under load now flow. This is what happens when hardware and software stop talking at each other and start co-authoring the playbook. The result?
A sovereign AI model that doesn’t just inference, it answers before the user finishes the question.
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Common Questions Answered
How did NVIDIA and Sarvam AI improve inference performance for sovereign AI models?
The collaboration delivered a 4x speedup in inference performance by combining kernel and scheduling optimizations on NVIDIA H100 SXM GPUs with the powerful compute capabilities of Blackwell architecture. They achieved this through NVFP4 weight quantization and optimizations that provided a 2x speedup on H100 GPUs and an additional 2x speedup with Blackwell, with even higher performance gains at interactive points.
What unique characteristics do Sarvam AI's sovereign foundation models possess?
Sarvam AI developed foundational models that support 22 Indian languages, English, math, and code, with model sizes ranging from 3B to 100B parameters. These models were specifically designed to maintain data sovereignty and serve India's diverse population, using NVIDIA Nemotron libraries and the NeMo Framework for training and optimization.
What key technologies did NVIDIA use to boost inference performance?
NVIDIA employed multiple optimization strategies, including the NVFP4 four-bit floating point format, multi-token prediction (MTP), and enhanced all-to-all communication primitives. These innovations allowed for significant increases in token throughput while maintaining model accuracy, particularly when running large models on platforms like the GB200 NVL72 and HGX B200.
Further Reading
- How NVIDIA Extreme Hardware-Software Co-Design Delivered a Large Inference Boost for Sarvam AI’s Sovereign Models — NVIDIA Developer Blog
- NVIDIA Blackwell Delivers 4x Inference Boost for India's Sarvam AI Sovereign Models — Blockchain News
- How NVIDIA Extreme Hardware-Software Co-Design Delivered a Large Inference Boost for Sarvam AI's Sovereign Models — NVIDIA Developer Forums
