Faster agentic AI systems on any hardware

Agentic workflows chain dozens of LLM calls per task - document processing, decision routing, tool use, verification loops. Every millisecond of latency compounds across the chain. MAX's compiled inference and Modular Cloud's dedicated endpoints keep each call fast and predictable. Forward-deployed engineers optimize the full pipeline for your specific workflow patterns.

Code agents generate, review, test, and iterate in tight loops - often 10-50 LLM calls per task. Speculative decoding and compiler-fused inference keep each call fast. Serve DeepSeek Coder, Qwen Coder, or your fine-tuned model on NVIDIA or AMD with the same compiled performance. Scale on Modular Cloud as your engineering team adopts the agent.

Real-time voice agents chain STT, reasoning, tool calls, and TTS into a single conversational turn. The user hears every millisecond of latency. MAX serves the full multimodal pipeline - speech, language, and audio models - from one container with compiler-fused performance. No stitching together separate inference stacks for each modality.

Some agent workflows can't leave the device - legal review on a laptop, field service on a tablet, classified environments with no connectivity. MAX compiles agent models natively for Apple Silicon and ARM CPUs. Run the reasoning loop locally with zero cloud dependency. Upgrade to cloud endpoints when connectivity and scale demand it.

Production multi-agent systems route tasks across specialized models - a planner, a coder, a reviewer, a summarizer. Each model needs low-latency serving with consistent throughput. Modular Cloud serves all of them from the same infrastructure with OpenAI-compatible endpoints. Mix model sizes across NVIDIA and AMD. Scale each agent independently. One platform, not four inference stacks.

Agentic workflows chain planning, reasoning, execution, and verification into sequential LLM calls. Latency stacks linearly - a 50ms improvement per call saves 2.5 seconds across a 50-step chain. MAX's compiled inference and continuous batching keep every step fast and predictable. Forward-deployed engineers profile your specific workflow patterns and optimize accordingly.

Run your agent stack on NVIDIA or AMD from the same container. Mix GPU types across agent roles - a large planner model on B200s, smaller tool-call models on MI355X for better price-performance. Shift workloads as pricing and availability change. No other agentic infrastructure supports multi-vendor GPU.

MAX compiles agent models natively for Apple Silicon and ARM CPUs. Run reasoning loops locally with zero cloud dependency - legal review on a laptop, field diagnostics on a tablet, classified environments with no connectivity. Same model, same code. Upgrade to Modular Cloud endpoints when scale demands it.

Agents need structured output - JSON, function signatures, typed tool calls - not free-form text. MAX's compiled inference generates constrained output faster because the compiler understands the output schema at graph level. Faster structured generation means faster agent loops, fewer retries, and more reliable tool execution.

Agents live and die by tool call latency. Every function call, API lookup, and retrieval step is an LLM round-trip. MAX's MLIR compiler optimizes the full inference path so each call completes faster - and in agentic loops with 10-50 calls per task, that compounds into seconds saved per interaction. Serve on Modular Cloud with dedicated endpoints for consistent, low-latency tool use.