SOPHGO AI Computing
SOPHGO BM1684X Edge AI Accelerator
BM1684X is positioned for high-density edge inference where video processing, neural-network acceleration and host connectivity must operate in one platform. It is relevant to customers building intelligent video analytics, compact inference servers and private on-premise AI appliances.
Stock, price and lead time: Contact GalaxyIC for confirmation.
BM1684X at a glance
The BM1684X belongs to SOPHGO's fourth-generation edge ai processor portfolio. Engineers evaluating this device should connect headline compute figures with the complete workload: model precision, memory footprint, video pipelines, host interfaces, latency, concurrency, thermal limits and software support. GalaxyIC uses the exact model identity in every inquiry so that the requested product is not confused with a related member of the same family.
- CPU
- 8-core Arm Cortex-A53 up to 2.3 GHz
- AI performance
- 32 TOPS INT8
- FP16 performance
- 16 TFLOPS
- FP32 performance
- 2 TFLOPS
- Memory interface
- 128-bit LPDDR4X up to 4266 Mbps
- Memory bandwidth
- Up to 68 GB/s
- Memory capacity
- Up to 16 GB
- Host interface
- PCIe Gen3 x16
- Video capability
- Multi-channel H.264/H.265 encode and decode
- Brand
- SOPHGO
- Supply status
- Contact for confirmation
Specifications shown here summarize the supplied SOPHGO company material. Final design decisions must use the current official data sheet, hardware manual and software release notes for the exact orderable device.
Target applications
SOPHGO BM1684X may be evaluated for multi-channel video analytics, smart-city and transportation inference, industrial visual inspection, edge large-language-model evaluation, private AI inference appliances. A suitable design-in depends on more than nominal AI performance. Camera count, resolution, codec load, preprocessing, model architecture, quantization, batch size, context length and peripheral traffic can materially change real throughput.
- Multi-Channel Video Analytics
- Smart-City And Transportation Inference
- Industrial Visual Inspection
- Edge Large-Language-Model Evaluation
- Private Ai Inference Appliances
For international projects, GalaxyIC recommends documenting the end application and the required validation stage. An evaluation board request, prototype build, pilot quantity and production purchase have different documentation, traceability and delivery requirements.
Engineering and integration checklist
- Confirm the required precision and compiled model format before hardware selection.
- Size memory from the deployed model, batch size and concurrent video channels rather than TOPS alone.
- Review PCIe topology, cooling, power budget and codec workload with the target carrier or server.
Before requesting volume pricing, confirm the exact part or platform, package, operating temperature, memory configuration, board form factor and supported software version. For AI workloads, include the model name, precision, input shape, expected latency or throughput, and number of concurrent streams or users. For RISC-V server projects, include the required operating system, compiler, firmware, networking, storage and expansion environment.
A proof of concept should test the full pipeline rather than an isolated TOPS figure. This includes data ingestion, decoding, preprocessing, inference, post-processing, storage and network output. It also reduces the risk of selecting a device whose theoretical compute is suitable but whose memory, interface or toolchain does not fit the deployed system.
How GalaxyIC handles a SOPHGO BM1684X RFQ
GalaxyIC is an independent electronic-component sourcing and RFQ service. We do not present this page as the official SOPHGO website and do not claim an authorized-distributor relationship. After receiving your inquiry, the sales team reconfirms the exact model identity, requested quantity, material condition, date or lot requirements when applicable, available documentation, lead time and commercial terms.
To receive a useful quotation, send the destination country, target quantity, project schedule and whether the request is for chips, modules, evaluation hardware, accelerator cards or complete systems. If alternatives are acceptable, state the performance, interface and software requirements that cannot change. No substitute should be approved from a marketing description alone.
SOPHGO ecosystem and platform context
SOPHGO develops AI processors, endpoint and edge vision devices, RISC-V server processors, accelerator modules, cards and server platforms. Products such as AS711 and AS713 illustrate how processor, memory, host CPU, storage, interconnect, thermal design and inference software come together as a deployable AI system. The right purchasing level therefore depends on whether the customer is building custom hardware or needs an integrated platform.
Software compatibility is a sourcing requirement, not an afterthought. Ask for the current compiler, runtime, framework support, model-conversion workflow and reference software that correspond to the selected hardware. Toolchain version, operator coverage and quantization accuracy can affect project schedules as much as physical lead time.
Frequently asked questions
Can GalaxyIC confirm BM1684X inventory immediately?
Inventory is not inferred from this SEO page. GalaxyIC confirms availability, quantity, condition, price and lead time after receiving an RFQ.
What should be included in the RFQ?
Include the exact model or suffix, quantity, application, destination, requested delivery date, package or temperature-grade requirements, and whether technical documentation or traceability is required.
Can BM1684X replace another AI processor?
Potential alternatives must be compared at system level. Compute precision, memory, video, I/O, power, form factor and software compatibility all require engineering review and written approval.