SOPHGO AI chips, edge processors and RISC-V server CPUs

Choose the product class before choosing the model

SOPHGO's portfolio spans endpoint AI vision processors, edge AI accelerators, cloud-oriented training and inference processors, and many-core RISC-V server CPUs. These categories solve different engineering problems. Endpoint devices emphasize integration, low power, camera input and local intelligence. Edge processors add memory, throughput, codec concurrency and host connectivity. Server products require a complete platform discussion covering memory, storage, networking, interconnect and software.

Use the pages below to narrow the product class, then confirm the exact device or family suffix through an RFQ. GalaxyIC does not publish assumed inventory or an unverified public price.

Endpoint, edge and cloud AI selection

Endpoint vision: SG2002 and CV180, CV181, CV184 and CV186 families

SG2002 combines RISC-V processing, an approximately 1 TOPS INT8 TPU, video codecs, ISP, sensor interfaces, audio and hardware security for endpoint AIoT vision. The CV family ranges from low-power embedded vision through higher-performance endpoint AI. These products are relevant to smart cameras, access control, retail terminals, industrial imaging and robotics. Always specify the exact suffix, package, temperature grade, ISP and interface requirements.

Edge inference: BM1684X, BM1688 and CV84X6

These processors target richer edge workloads such as multi-channel video analysis, multimodal inference and compact private AI systems. A fair comparison must include numerical precision, memory capacity and bandwidth, codec workload, host interfaces, thermal design and supported model toolchain.

Large-model computing: BM1690E, AS711 and AS713

BM1690E is positioned for integrated large-model training and inference. SOPHGO's AS711 and AS713 systems provide platform context for large-memory model deployment. Customers should identify model size, precision, context length, concurrency, latency and throughput targets before requesting a configuration.

RISC-V servers: SG2042 and SG2044

SG2042 and SG2044 address native RISC-V server computing. Procurement may involve processors, boards, development systems or complete servers, so the RFQ should define the required hardware level, Linux environment, compiler, firmware, storage and networking.

Information that improves quotation quality

Send the exact SOPHGO model, target quantity, destination country and project schedule. Add the application, model framework, precision, memory need, video channels, host interface and environmental requirements where relevant. State whether you require traceability, inspection records, technical documents, evaluation hardware or production material.

GalaxyIC independently reconfirms availability, material condition, price and lead time. This website is not the official SOPHGO website and does not claim an authorized-distributor relationship. Product specifications must be checked against current SOPHGO documentation before design release.

SOPHGO AI chip FAQ

Which SOPHGO chip is suitable for edge AI?

BM1684X, BM1688 and CV84X6 cover different levels of edge AI performance, memory, media processing and numerical precision. The correct choice depends on the actual model and system workload.

Can GalaxyIC quote SOPHGO processors for overseas delivery?

Send the exact model, quantity and destination. GalaxyIC will confirm sourcing status, commercial terms and delivery feasibility for the specific request.

Are prices and quantities shown publicly?

No assumed inventory or price is published on these pages. Stock, condition, price and lead time are confirmed after inquiry.

Can SOPHGO devices be used as NVIDIA or Intel alternatives?

They may be evaluated in some workloads, but compatibility is not automatic. Hardware interfaces, numerical precision, memory, software and application performance require engineering validation.