You will get I will deploy 100B+ Large Language Models on NVIDIA DGX Spark, Blackwell
Project details
I specialize in deploying high-parameter Large Language Models (100B+) on specialized hardware, specifically the NVIDIA DGX Spark and Blackwell architectures. I provide a complete, containerized environment using vLLM and NVIDIA NIM, optimized for the unique ARM64/aarch64 Linux kernel. My deployments are tuned for maximum performance, achieving throughputs of up to 1,400 tokens/second and generation speeds of 16 tokens/second on 120B models. Whether you need a simple inference setup or a full agentic framework integration with OpenClaw, I ensure a stable, production-ready system with a 64,000-token context window.
AI Development Type
Recommendation SystemAI Tools
NVIDIA AI PlatformAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$195
|
Standard
$445
|
Advanced
$795
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | - | |
Knowledge Graph | - | - | - |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | - | - | |
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $150
Additional Revision
+$100
Secure Remote Access Setup (Tailscale)
(+ 1 Day)
+$50
Peace of Mind
+$150About Muaz
Senior AI Architect | Claude Code Certified | DGX Spark & Blackwell
Rawalpindi, Pakistan - 1:27 pm local time
I'm one of them.
I specialize in deploying large language models on NVIDIA DGX Spark (Blackwell GB10) and Apple Silicon — remotely, privately, and production-ready. My recent DGX Spark deployment achieved 16.4 tokens/sec on Nemotron-3-Super 120B with a 64k context window, full auto-restart, and zero cloud dependency.
I hold the Anthropic Claude Code 101 Certification — meaning I don't just deploy models, I architect decision-capable agents that handle multi-step reasoning and complex tool-calling without the hallucination loops that uncertified builds fall into.
What I actually solve:
— Kernel panics from memory over-allocation on unified memory systems
— CUDA illegal instruction crashes on Blackwell SM121 chips
— Docker symlink failures in NVIDIA NIM deployments
— OpenClaw and NemoClaw misconfigurations causing 400 errors
— SSH lockouts from GPU power throttling during first-token warmup
My stack:
DGX Spark · Blackwell SM121 · Apple Silicon M4 · vLLM v0.17+ · NVIDIA NIM · CUDA 13.0 · OpenClaw · NemoClaw · MCP · Docker ARM64 · Tailscale · RAG Pipelines · n8n
If you've invested in serious AI hardware and need someone who has already solved the hard problems — not someone who will learn on your machine — let's talk.
Steps for completing your project
After purchasing the project, send requirements so Muaz can start the project.
Delivery time starts when Muaz receives requirements from you.
Muaz works on your project following the steps below.
Revisions may occur after the delivery date.
System Audit & Environment Preparation
I perform a full audit of your NVIDIA GPU drivers (580.142+) and CUDA version (13.0) to ensure compatibility with the Blackwell SM121 architecture. I then configure the Docker environment specifically for ARM64/aarch64 systems.
Optimized Model Deployment
I deploy the selected model (e.g., Nemotron-3-Super 120B) using NVFP4 quantization to fit within VRAM limits while maintaining speed. I configure the vLLM inference engine for peak throughput and verify the 64k context window stability.