AI Workstation Supplier — Local LLM, Private AI, NVIDIA RTX Ada and DGX Spark

Haink supplies AI workstations and compact AI servers for small teams running local AI workloads in Hong Kong, Dubai, and Mainland China. Available platforms include NVIDIA DGX Spark personal AI supercomputers, NVIDIA RTX Ada Generation professional GPU workstations, and GPU-equipped workstation platforms from Dell, HP, Lenovo, and Supermicro — configured for local large language model inference, retrieval-augmented generation, private code assistants, and small-scale model fine-tuning without cloud dependency.

An AI workstation is the right solution for teams that need capable AI compute locally — for data privacy, regulatory compliance, network latency, or cost reasons — without the infrastructure overhead of a full enterprise AI server cluster. A single workstation with one or two high-end professional GPUs can run 7B to 70B parameter language models locally, serve a RAG pipeline to a team of 10–30 users, or fine-tune a small domain-specific model on proprietary data without sending that data to any external service.

Who Needs an AI Workstation

AI workstations are the right procurement choice for the following situations:

Small development teams (3–20 people) running a private LLM or code assistant that must not reach cloud APIs due to data sensitivity, IP protection, or regulatory requirements
Legal, finance, healthcare, or government organizations that need AI capabilities on confidential documents without any data leaving the premises
Research teams or university labs that need dedicated local GPU compute for model experimentation, evaluation, and fine-tuning without shared cloud infrastructure
Organizations evaluating AI before committing to a larger infrastructure investment — a workstation provides a low-risk entry point to learn operational requirements and model selection
Branches or regional offices of larger enterprises that need local AI inference for low-latency applications without depending on a remote data center connection
AI startups and ISVs building and testing AI-powered products that require a reproducible local inference environment

NVIDIA DGX Spark — Personal AI Supercomputer

NVIDIA DGX Spark is NVIDIA's personal AI supercomputer designed specifically for individual researchers, developers, and small teams who need serious AI compute in a compact desktop form factor. DGX Spark is powered by the NVIDIA GB10 Grace Blackwell Superchip — the same Blackwell GPU architecture used in NVIDIA's data center H200 and B200 platforms — in a single self-contained unit that sits on a desk.

NVIDIA DGX Spark specifications and capabilities:

NVIDIA GB10 Grace Blackwell Superchip — combines a Blackwell GPU with a Grace ARM CPU in unified NVLink-C2C architecture with 1 TB/s CPU-GPU memory bandwidth
128 GB unified LPDDR5X memory shared between CPU and GPU — sufficient to run 70B parameter models like Llama 3.3 70B and DeepSeek-R1 70B natively at full precision, or 405B models at INT4 quantization
1 PFLOPS FP8 AI compute — purpose-built for transformer inference workloads
Compact desktop form factor — roughly the size of a Mac Studio; no special power or cooling infrastructure required, plugs into standard 100–240V outlet
ConnectX-7 400G networking — DGX Spark units can be interconnected via NVLink Interconnect Switch to form a small multi-node cluster when additional capacity is needed
Ships with NVIDIA AI Enterprise software stack including NIM microservices, CUDA, TensorRT-LLM, and cuDNN pre-installed

DGX Spark is Haink's primary recommendation for teams that need to run large language models locally with the lowest possible setup complexity and the highest single-unit performance available in a desktop form factor. It is the first product that makes running a full-quality 70B model on a desktop genuinely practical without quantization quality loss.

NVIDIA RTX Ada Generation Professional GPU Workstations

NVIDIA RTX Ada Generation professional GPUs (formerly Quadro RTX) are the standard choice for AI workstations built on desktop workstation platforms. RTX Ada GPUs are designed for professional workstation use with ECC memory, larger VRAM than consumer GeForce cards, and validated drivers for enterprise software stacks.

NVIDIA RTX Ada GPU Options for AI Workstations

NVIDIA RTX 4000 Ada Generation — 20 GB GDDR6 ECC, 192-bit memory bus; suitable for running 7B–13B parameter models comfortably and 34B models with quantization; the entry-level professional AI workstation GPU
NVIDIA RTX 4500 Ada Generation — 24 GB GDDR6 ECC; more headroom for 13B–34B models with better quantization quality; good balance of VRAM and cost for small team deployments
NVIDIA RTX 5000 Ada Generation — 32 GB GDDR6 ECC, PCIe 4.0 x16; runs 34B models cleanly and handles 70B models at 4-bit quantization with good throughput; suitable for a shared team inference server
NVIDIA RTX 6000 Ada Generation — 48 GB GDDR6 ECC; Haink's recommended single-GPU workstation configuration for teams needing maximum local LLM capability; runs 70B models at 4-bit quantization with high throughput, or smaller models at full precision; two RTX 6000 Ada cards in NVLink provide 96 GB combined VRAM for running 70B+ models at higher quality

GPU VRAM and Model Size Reference

7B model (FP16) — requires ~14 GB VRAM; fits on RTX 4000 Ada or higher
13B model (FP16) — requires ~26 GB VRAM; fits on RTX 5000 Ada or dual RTX 4500 Ada
34B model (FP16) — requires ~68 GB VRAM; requires dual RTX 6000 Ada (96 GB) or quantization
70B model (INT4 quantization) — requires ~35–40 GB VRAM; fits on dual RTX 5000 Ada (64 GB) or single RTX 6000 Ada (48 GB with aggressive quantization)
70B model (FP16) — requires ~140 GB VRAM; requires DGX Spark (128 GB unified) or multi-GPU server

Workstation Platforms

NVIDIA RTX Ada GPUs are installed in professional workstation chassis that provide the PCIe bandwidth, thermal headroom, and power delivery for one or two full-length double-width GPU cards. Haink supplies AI-configured workstations on the following platforms:

Dell Precision 7960 Tower — dual Intel Xeon Scalable or single Intel Xeon W workstation chassis with up to two PCIe 5.0 double-width GPU slots and up to 2 TB DDR5 RAM; the standard enterprise-grade workstation platform for dual RTX 6000 Ada AI configurations
HP Z8 Fury G5 — Intel Xeon W9-3595X or dual Intel Xeon Scalable workstation with up to four double-width GPU slots and 2 TB DDR5 RAM; suitable for multi-GPU AI workstation configurations with three or four RTX Ada cards
Lenovo ThinkStation P8 — AMD Threadripper PRO 7000 series workstation with up to two double-width GPU slots, 2 TB DDR5 ECC RAM, and PCIe 5.0 connectivity; the AMD-platform alternative for AI workstation deployments
Lenovo ThinkStation P7 — Intel Xeon W9-3595X workstation with dual GPU support and enterprise reliability for professional AI development environments
Supermicro SYS-741GE-TNRT / SYS-540GQ-TNTRT — rackmount workstation-class 1U and 4U GPU servers for organizations that prefer rack-mount form factors while keeping single-team scale; supports two to eight NVIDIA RTX Ada or L40S GPUs

NVIDIA L40S — Rack-Mount AI Inference Server Option

For teams that prefer a rackmount form factor or need to serve AI inference to more than 20–30 concurrent users, NVIDIA L40S is the bridge between a workstation and a full data center GPU server. L40S is an Ada Lovelace architecture GPU with 48 GB GDDR6 ECC, designed for AI inference and graphics in server environments. A 1U or 2U server with two L40S cards (96 GB total VRAM) provides substantially more inference throughput than a workstation while remaining compact and relatively simple to operate.

NVIDIA L40S — 48 GB GDDR6 ECC, 362.05 TFLOPS FP8, PCIe Gen4 passive cooling; server form factor equivalent of RTX 6000 Ada optimized for inference density
Dual L40S in 1U (e.g., Supermicro SYS-111E-FWTR) — 96 GB total VRAM for 70B model inference with good throughput at inference concurrency levels needed by teams of 20–100 users

Use Cases Supported by AI Workstations

Local LLM Inference — Private ChatGPT Alternative

Running an open-source LLM locally (Llama 3.3, Mistral, DeepSeek-R1, Qwen2.5, Gemma) using inference frameworks such as Ollama, LM Studio, or vLLM. A single workstation with an RTX 6000 Ada or DGX Spark can serve a private LLM to a team of 10–30 users via a local API endpoint, providing ChatGPT-class capability on internal documents without any data leaving the premises.

RAG — Retrieval-Augmented Generation

Connecting a local LLM to a vector database (Qdrant, Chroma, Weaviate, Milvus) populated with company-specific documents, product manuals, contracts, or knowledge bases. RAG allows team members to query proprietary knowledge in natural language with AI-generated answers grounded in internal documents. A single GPU workstation handles both the embedding generation and LLM inference steps for small to medium team deployments.

Private Code Assistant

Running a locally hosted code assistant model (DeepSeek Coder, Qwen2.5-Coder, CodeLlama) integrated with VS Code, JetBrains IDEs, or Cursor via a Continue.dev or similar plugin. All code context remains local — no proprietary code is sent to GitHub Copilot, OpenAI, or any external API. Suitable for development teams working on proprietary software, financial systems, or security-sensitive codebases.

Document Intelligence and Classification

Processing confidential documents with local AI for classification, extraction, summarization, and analysis. Legal firms, financial institutions, and compliance teams use local AI workstations to apply LLM-based processing to client documents, contracts, and regulatory filings without external data exposure.

Fine-Tuning Small and Medium Models

Running supervised fine-tuning (SFT) or parameter-efficient fine-tuning (LoRA, QLoRA) on 7B–13B base models using a company's proprietary data to create a domain-specific model. A workstation with one or two RTX 6000 Ada GPUs and 256 GB system RAM is sufficient for QLoRA fine-tuning of 7B–34B models on datasets of tens of thousands of examples. Suitable for organizations building custom AI models on proprietary terminology, style, or domain knowledge.

Where Haink Supplies AI Workstations

Hong Kong — NVIDIA DGX Spark, RTX Ada workstations, and GPU servers delivered duty-free to Hong Kong enterprises, research institutions, and startups. AI workstation supplier Hong Kong →
Dubai — AI workstations and compact GPU servers delivered through Dubai free trade zone logistics to UAE enterprises, financial institutions, and tech companies. AI workstation supplier Dubai →
Mainland China — AI workstation platforms delivered to enterprises, research teams, and development organizations in Beijing, Shanghai, Shenzhen, and other major cities with full import coordination. AI workstation supplier Mainland China →

Related Resources

Frequently Asked Questions

Who supplies AI workstations in Dubai?

Haink supplies AI workstations and compact GPU servers — including NVIDIA DGX Spark, RTX 6000 Ada and RTX 5000 Ada workstations on Dell Precision, HP Z8, and Lenovo ThinkStation platforms — to enterprises, financial institutions, and technology companies in Dubai and the UAE. Haink coordinates procurement and delivery through Dubai free trade zone logistics.

Where can I buy a local AI workstation in Hong Kong?

Haink delivers NVIDIA DGX Spark personal AI supercomputers, NVIDIA RTX Ada Generation GPU workstations, and NVIDIA L40S compact inference servers to organizations in Hong Kong. Hong Kong's free port status means no import duties on AI workstation hardware. Haink coordinates sourcing and direct delivery to enterprise and research facilities in Hong Kong.

What is the NVIDIA DGX Spark and who is it for?

NVIDIA DGX Spark is a personal AI supercomputer powered by the NVIDIA GB10 Grace Blackwell Superchip with 128 GB unified memory. It is designed for individual AI researchers, developers, and small teams who need to run large language models locally — including 70B parameter models at full precision — in a compact desktop form factor without data center infrastructure. DGX Spark plugs into a standard power outlet and ships with NVIDIA's full AI software stack pre-installed. It is the right choice for teams that want the highest single-unit local AI capability with the lowest setup complexity.

What size LLM can I run on an RTX 6000 Ada workstation?

The NVIDIA RTX 6000 Ada has 48 GB GDDR6 ECC VRAM. At 4-bit quantization (INT4/Q4_K_M), a 70B parameter model requires approximately 38–42 GB VRAM, fitting within a single RTX 6000 Ada. At FP16 precision, a 13B model requires approximately 26 GB and fits comfortably. For dual RTX 6000 Ada (96 GB combined via NVLink), 70B models run at 4-bit with significantly higher throughput, and smaller models run at full FP16 precision. For 70B at full FP16 without any quantization, the NVIDIA DGX Spark (128 GB unified memory) is the appropriate choice.

What is the difference between a consumer GeForce GPU and an RTX Ada professional GPU for AI?

NVIDIA RTX Ada professional GPUs differ from consumer GeForce in three important ways for AI workstation use: ECC memory (Error Correcting Code) prevents silent data corruption in long-running AI inference and training jobs; larger VRAM capacities (up to 48 GB on RTX 6000 Ada vs 24 GB on the consumer RTX 4090) allow larger models to run without quantization; and professional driver certification ensures stability in enterprise software environments. For production AI workloads serving a team, professional GPUs are the appropriate choice. Consumer GeForce GPUs are sufficient for personal experimentation but lack ECC memory and sufficient VRAM for many production use cases.

How many users can a single AI workstation serve?

A workstation with a single RTX 6000 Ada running a 7B model (e.g., Llama 3.1 8B or Mistral 7B) via vLLM can typically handle 10–30 concurrent users with acceptable response latency for chat and document Q&A use cases. A workstation with two RTX 6000 Ada GPUs running a 34B model can serve 20–50 concurrent users. For larger teams or heavier workloads, a rack server with NVIDIA L40S or H100 GPUs is more appropriate. Haink can advise on the right configuration based on team size, model choice, and expected concurrency.

Can I run DeepSeek, Llama, or Qwen on an AI workstation?

Yes. Open-source models including DeepSeek-R1 (7B, 14B, 32B, 70B), Llama 3.3 70B, Qwen2.5 (7B, 14B, 32B, 72B), Mistral, Gemma 2, and Phi-4 run on NVIDIA RTX Ada GPU workstations using locally installed inference frameworks such as Ollama, vLLM, LM Studio, or llama.cpp. Model size and quantization level determine which GPU is appropriate. Haink can recommend the correct GPU configuration for the specific models a team plans to run.

What is the difference between an AI workstation and an AI server for small teams?

An AI workstation (tower form factor, single GPU or dual GPU) sits on or under a desk, runs standard desktop power (1000–1600W), requires no special cooling, and is managed like a workstation. An AI server (1U or 2U rack mount, two to eight GPUs) requires a server rack, higher power circuits, and data center or server room installation. For teams without a server room, a workstation or DGX Spark is the practical choice. For teams with a rack or wiring closet and needing to serve more concurrent users, a compact rackmount server with L40S GPUs is the next step up. Both serve the same local AI purpose; the difference is infrastructure context and scale.

Can Haink deliver an AI workstation to Mainland China?

Yes. Haink supplies NVIDIA RTX Ada Generation GPU workstations on Dell Precision, HP Z8, and Lenovo ThinkStation platforms to enterprises and research organizations across Mainland China, with full import documentation and customs clearance. Note that NVIDIA's export control regulations affect availability of certain high-end GPU products for China delivery — Haink advises on currently available configurations for Mainland China on a per-inquiry basis.