When moving inference from the cloud back on-premises, the first question is usually not "whether" but "how much." The Junhangclaw inference workstations come in four tiers, from 50 TOPS to 2,070 TFLOPS — and what separates the tiers is not just the compute number, but the chip route and the users each tier actually fits.
The Four Tiers at a Glance
| Model | Compute | Who It Fits |
|---|---|---|
| JH-W50 Entry | 50 TOPS | Individual users running local inference |
| JH-W120 Enhanced | 120 TOPS | Small teams; lightweight model deployment |
| JH-W280 Professional | 280 TOPS (INT8) | Government and enterprise xinchuang scenarios |
| JH-W2000 Flagship | 2,070 TFLOPS (FP4 sparse) | Desktop-class local large-model inference |
| JH-W2000N Extended | Same as W2000 | Local data-drive expansion (adds four 2.5-inch bays) |
What Each of the Three Chip Routes Solves
The W50 uses a domestic chip, bringing the entry barrier for local inference within reach of individual users. The W280 uses a MetaX domestic chip; its 280 TOPS of INT8 compute paired with a domestic-silicon route squarely targets the supply-chain autonomy requirements of government and enterprise xinchuang projects. The W2000 opts for an NVIDIA Blackwell GPU with a 14-core Arm Neoverse CPU, prioritizing peak compute and a mature software ecosystem — mainstream inference frameworks and quantization toolchains work out of the box. Choose the route before the compute number: in a xinchuang project no amount of TOPS substitutes for domestic-silicon status, while ecosystem-first teams are rarely willing to pay the migration cost of switching toolchains.
128GB Unified Memory: The Key Variable for Running Large Models on the W2000
The first bottleneck in local inference is often memory capacity, not compute: if the model weights do not fit, no amount of TFLOPS helps. The W2000's 128GB of LPDDR5x unified memory lets the CPU and GPU share a single address space — weights need not shuttle between system RAM and discrete VRAM, and capacity is not hard-partitioned the way traditional graphics memory is. For quantized large-parameter models, that means a bigger model or a longer context can reside entirely on one machine. A 140mm-radiator liquid cooler keeps sustained inference loads inside the compact 199.3×199.3×100.3mm chassis, with 1TB of storage for the system and working models; when corpora and vector databases need to live locally with the machine, the W2000N's four 2.5-inch bays provide the room.
Data Kept On-Premises: Who Should Treat It as the First Criterion
All four tiers share one positioning: local inference with private data kept on-premises. For fields such as legal, healthcare, finance, and government, prompts, corpora, and inference outputs are themselves sensitive assets; local deployment shrinks the compliance boundary from a cloud provider's contract terms to the physical boundary of one device, and work like knowledge-base Q&A and document review can run fully offline. When selecting, follow a fixed order: first fix the data boundary (must it stay on-premises, or can it go to the cloud), then the model scale (which determines the memory and compute tier), and only then form factor and budget. Followed in that order, most individual users land on the W50 or W120, xinchuang projects land on the W280, and teams that need to run large-parameter models on a desktop have exactly two options among the tiers: the W2000 and W2000N.