Last updated 08-21-2026
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SiMa.ai
SiMa.ai builds a co-optimized hardware and software stack for physical AI workloads that must run on the edge. The platform pairs Modalix MLSoC silicon with Palette software so teams can compile ONNX models, build perception pipelines, and deploy to robots, vehicles, drones, and factory systems without sending sensor data to the cloud.
Where general-purpose GPUs trade watts for flexibility, SiMa.ai targets fixed edge deployments with a purpose-built MLSoC rated at 50 TOPS under 10 watts and a compiler that outputs .sima binaries in one API call. Palette Neat adds an agentic IDE layer that collapses typical physical AI timelines from months to days, which matters when you are iterating on hardware that cannot tolerate cloud round trips.
Embedded ML engineers, robotics developers, and industrial automation teams use SiMa.ai when they need CNN, ViT, and LLM inference on Modalix hardware with pin-compatible Jetson Orin upgrades. Markets listed on the site include automotive, aerospace and defense, healthcare, retail, and government programs that require deterministic on-device inference.
Modalix MLSoC delivers 50 TOPS at under 10 watts for CNN, ViT, and LLM workloads
Palette SDK compiles 200+ ONNX and Hugging Face models to .sima binaries in one API call
Mixed-precision quantization targets under 1% accuracy delta with INT8, BF16, and hybrid policies
Modalix SoM starts at $449 and is pin-compatible with NVIDIA Jetson Orin NX and Nano boards
Modalix DevKit costs $1,499 with carrier board, SoM, PSU, and 500GB NVMe plus Palette pre-installed
Palette Neat agentic IDE runs in ghcr.io/sima-neat/elxr container with compile, run, and deploy workflows
Shadow inference pass on MLSoC hardware validates latency before the final binary is emitted
Co-designed Modalix silicon and Palette software remove glue chips and external accelerators from edge stacks
Palette SDK compiles ONNX models with mixed-precision quantization and under 1% accuracy delta
Modalix SoM is pin-compatible with NVIDIA Jetson Orin boards for drop-in hardware upgrades
Palette Neat open-source environment on GitHub with a full Developer Center and Model Browser
Hardware purchases required; no cloud-only or free software tier for production deployment.
Chip-down and PCIe card pricing require contacting sales rather than self-serve checkout.
Palette Neat and SDK target Modalix silicon, not general-purpose GPU or CPU hardware.
What is Palette Neat on SiMa.ai?
Palette Neat is SiMa.ai's agentic development environment for physical AI. It combines model compilation, Python and C++ app runners, and deployment tools in a containerized IDE so developers can move from ONNX assets to working edge applications in days instead of months.
What hardware does SiMa.ai sell?
SiMa.ai sells the Modalix MLSoC chip, a Modalix SoM starting at $449, a half-height PCIe card, and a Modalix DevKit priced at $1,499 that includes a carrier board, SoM, PSU, and 500GB NVMe storage with Palette software pre-installed.
How much performance does Modalix MLSoC provide?
Modalix MLSoC on SiMa.ai is rated at 50 TOPS while drawing under 10 watts. The chip supports BF16, INT8, and INT16 inference for CNN, vision transformer, and LLM workloads on edge devices where cloud latency is not an option.
Which industries does SiMa.ai target?
SiMa.ai lists drones, robotics, automotive, industrial automation, government, smart retail, and healthcare as core markets. The platform targets perception, autonomy, and on-device inference where deterministic edge performance matters more than flexible GPU programming.
Does SiMa.ai support ONNX model compilation?
SiMa.ai Palette SDK compiles ONNX models directly to MLSoC binaries in a single API call. The compiler handles mixed-precision quantization, memory layout for MLA architecture, and supports 200+ ONNX-compatible and Hugging Face models including ResNet, YOLO, SegFormer, Gemma, and Qwen variants.
Is SiMa.ai hardware compatible with Jetson boards?
SiMa.ai Modalix SoM modules are pin-compatible with NVIDIA Jetson Orin NX and Nano carrier boards. Teams can evaluate 50 TOPS edge inference on SiMa.ai silicon as a drop-in upgrade path without redesigning their existing Jetson-based hardware layouts.