QUASAR Ryzen AI Max 4L Desktop Workstation

Overview

An internal PSU changes what a small machine can do. A 350 W Flex power supply inside the chassis means no adapter brick, no cable clutter under the desk, and a stable power envelope for all-day work — in a 4 L box measuring 248 × 188 × 98 mm. Up to 128 GB unified memory feeding a 40-CU Radeon 8060S and a 50 TOPS NPU.

Key highlights

• Internal 350 W Flex PSU — a single mains lead, no external adapter.
• Up to 128 GB LPDDR5X-8000 (256-bit), up to 96 GB assignable to graphics.
• Ryzen AI Max+ 395 — 16C/32T, 5.1 GHz boost, Radeon 8060S, 50 TOPS NPU.
• Full desktop I/O — USB4, USB 3.2 Gen2 Type-A, HDMI 2.1 FRL, DP 1.4, 2.5GbE, SD card.
• Optional fingerprint power button and LED bar for managed fleets.
• 4 L, 248.5 × 188.4 × 97.5 mm — deskside without being a tower.
• Windows 11 or Ubuntu, imaged and validated by RDP before dispatch.

AI workload fit

• All-day local inference as a primary workstation.
• Fine-tuning and adapter training on small to mid models.
• RAG over private corpora held on the machine.
• Agentic AI and application development.
• Generative AI for image, document and text production.
• NLP & speech transcription and summarisation pipelines.

AI workload positioning

This is the configuration to standardise on when the machine is someone’s primary workstation rather than an AI appliance beside one — internal power, full I/O, and enough thermal envelope to work all day. The honest boundary is unchanged across this family: unified memory decides which model fits, and an integrated GPU with a 50 TOPS NPU is an inference and light-fine-tuning engine, not a training accelerator. When training throughput becomes the constraint, step to a discrete-GPU workstation.

Industry use cases

• Enterprise & GCCs: a standard-issue AI workstation for applied-AI and platform teams.
• BFSI: quant and analyst desktops running local models on regulated data.
• Healthcare: clinical informatics workstations kept inside the hospital network.
• Public sector & sovereign: departmental AI desktops with on-soil residency.
• Research & education: per-researcher machines that run real models without cluster time.

Performance & how to be sure

On this class the decisive figures are unified memory capacity and bandwidth — roughly 256 GB/s across a 256-bit bus — because they set which model fits and how fast it decodes. Rather than quote a tokens/sec figure that will not match your workload, RDP offers a “benchmark your model” session: bring the model, precision and context length you actually intend to run, and we will validate it on this exact configuration before you commit.

Series & upgrade path

QUASAR performance tier. Below: the CARINA 1L and 2L compact units. Beside: the 2L Liquid-Cooled AI PC when sustained thermals matter more than I/O. Above: RDP AI Workstations with discrete RTX PRO GPUs, then GPU servers. Compare with NVIDIA DGX Spark where CUDA parity with production servers is the requirement.

On-prem vs cloud (TCO)

For sustained daily AI work this configuration removes per-GPU-hour billing, queueing for scarce instances, and egress charges on your own data — and keeps everything on-premises for DPDP and data-residency obligations. Cloud still wins for burst capacity and one-off very large training runs; on-prem wins on sustained utilisation, control and predictable INR capital cost. We model the crossover with your actual usage rather than assert it.

Software & day-one readiness

Ships with Windows 11 or Ubuntu and the AMD AI stack — ROCm, DirectML and ONNX Runtime — with local model runtimes (LM Studio, Ollama, llama.cpp) validated before dispatch. The GPU draws from the same unified pool as the CPU, so you assign memory to the model rather than shopping for a card with enough VRAM. Migration from a CUDA workflow is a real exercise and RDP scopes it honestly rather than calling it a drop-in swap.

Power, thermal & acoustics

Internal 350 W Flex PSU on a standard 230 V socket — one mains lead, no adapter. Air-cooled with an acoustic profile suited to an occupied office; no facility work. Exact wattage, BTU and dB(A) figures come from RDP bench measurement rather than estimates — ask for the site-readiness sheet with your quote.

Deployment, warranty & support

Built to order by RDP Technologies. Supplied with GST invoice, pan-India onsite support, and availability through GeM for public-sector procurement. Built to order — typically 4–6 weeks.

Why RDP

RDP Technologies is a Make-in-India OEM with 14+ years and 300,000+ units shipped, supplying AI infrastructure from desk-side systems to rack-scale AI factories — with predictable INR pricing, GST invoicing and pan-India onsite service.

Buy with confidence

Use Request a Quote to reach an RDP solution architect for sizing, a benchmark-your-model session, financing options and a delivery plan. No obligation.

Specifications