Inference Benchmarking

This report analyzes a high-performance deployment of DeepSeek Mixture-of-Experts (MoE) models on a dual Nvidia DGX Spark cluster. By leveraging multi-node orchestration, the setup achieved a remarkable 40tk/s single-stream inference speed at 1M context length, with an aggregate throughput of 350tk/s. This benchmark establishes a new ceiling for local LLM hosting, significantly outperforming high-end setups like the RTX Pro 6000 and Mac M2 Ultra (192GB). ▶ Hardware Synergy: The dual-cluster configuration overcomes memory bandwidth bottlenecks inherent in MoE models, bringing local inference speeds in line with premium commercial APIs. ▶ Performance Gap: Under 1M context stress tests, the DGX cluster demonstrates superior stability and throughput compared to Apple's Unified Memory Architecture, proving the necessity of dedicated compute clusters for complex RAG and long-form reasoning. ▶ Agentic Viability: A 40tk/s output rate enables local AI agents to ingest and analyze massive datasets in near real-time, effectively eliminating latency hurdles for production-grade local deployments. Bagua Insight At Bagua Intelligence, we see this as a pivotal shift: the local LLM meta is moving from "feasibility" to "production-grade velocity." As DeepSeek continues to dominate the open-weights landscape, enterprise hardware requirements are pivoting toward multi-node, high-interconnect architectures. The DGX Spark results prove that for privacy-sensitive sectors like finance or legal, a dual-node cluster is now a viable, high-performance alternative to costly cloud-based inference. Furthermore, this highlights the physical limitations of consumer-prosumer hardware (like the Mac M2 Ultra) when faced with enterprise-scale MoE workloads—bandwidth is the ultimate bottleneck. Actionable Advice 1. Cluster over Capacity: Enterprises deploying DeepSeek-class models should prioritize multi-node interconnects (NVLink/RoCE) over simply stacking VRAM in a single chassis. 2. Quantization Strategy: Implement FP8 or advanced quantization kernels to optimize the trade-off between memory footprint and inference latency. 3. Benchmark for Agents: When evaluating local hardware, use token-per-second metrics at 100k+ context windows as the primary KPI, as this dictates the actual utility of Agentic workflows.

Executive Summary Recent community benchmarks on the RTX 4090 reveal that Google’s Gemma 4 12B model delivers complex coding and logical reasoning performance that rivals its 26B sibling, setting a SOTA benchmark for local deployment efficiency. ▶ VRAM Efficiency: The 12B variant operates within a 9GB VRAM footprint at 80 tok/s, making high-tier GenAI accessible to mid-range consumer hardware. ▶ Reasoning Parity: In stress tests involving multi-component physics simulations (Galton boards, chaotic pendulums), the 12B model demonstrated zero-shot coding logic nearly indistinguishable from the 26B version. Bagua Insight Google is effectively weaponizing "parameter efficiency" to disrupt the local LLM ecosystem. The Gemma 4 12B isn't just a smaller model; it’s a strategic strike against the "bigger is better" narrative. By achieving logical parity with the 26B model in high-entropy tasks like physics-based HTML5 coding, Google is signaling that architectural optimization and distillation have reached a tipping point. While the 26B-A4B model offers superior throughput (138 tok/s), the 12B version hits the "sweet spot" for the developer desktop. This move directly challenges Meta’s Llama 3 dominance in the mid-size segment by offering a more favorable performance-to-VRAM ratio, essentially democratizing high-end AI development for users with standard 12GB/16GB GPUs. Actionable Advice For Developers: Pivot local prototyping workflows to Gemma 4 12B. It provides the best balance of logic and latency for 90% of coding automation tasks without saturating high-end VRAM. For Enterprise Architects: Prioritize 12B fine-tuning for edge-based RAG applications. The marginal gains of the 26B model in logic do not justify the additional hardware overhead for most localized business logic. Hardware Strategy: While the RTX 4090 remains the gold standard, the 12B’s optimization makes the RTX 4070 Ti/4080 series highly viable for professional-grade AI development.