Futures
Access hundreds of perpetual contracts
CFD
Gold
One platform for global traditional assets
Options
Hot
Trade European-style vanilla options
Unified Account
Maximize your capital efficiency
Demo Trading
Introduction to Futures Trading
Learn the basics of futures trading
Futures Events
Join events to earn rewards
Demo Trading
Use virtual funds to practice risk-free trading
CFD
U.S. stock CFD derivatives
US Stocks
Access real US stocks and ETFs
HK Stocks
Trade quality Hong Kong-listed stocks
Korean Stocks
SK Hynix
Real Korean stocks and top assets
Stock Futures
High leverage, 24/7 trading
Tokenized Stocks
Backed by real stock assets
IPO Access
Unlock full access to global stock IPOs
GUSD
Mint GUSD for Treasury RWA yields
Stocks Activities
Trade Popular Stocks and Unlock Generous Airdrops
Launch
CandyDrop
Collect candies to earn airdrops
Launchpool
Quick staking, earn potential new tokens
HODLer Airdrop
Hold GT and get massive airdrops for free
IPO Access
Unlock full access to global stock IPOs
Alpha Points
Trade on-chain assets and earn airdrops
Futures Points
Earn futures points and claim airdrop rewards
Promotions
AI
Gate AI
Your all-in-one conversational AI partner
Gate AI Bot
Use Gate AI directly in your social App
GateClaw
Gate Blue Lobster, ready to go
Gate for AI Agent
AI infrastructure, Gate MCP, Skills, and CLI
Gate Skills Hub
10K+ Skills
From office tasks to trading, the all-in-one skill hub makes AI even more useful.
/ 🧠 Why can the future personal AI computer (like NVIDIA DGX Spark) really compete with data centers?
It's not because desktops are becoming powerful enough to replace the cloud, but because AI's "demand structure" is splitting —
Training stays in the cloud, inference returns to the local.
2/ Key breakthrough #1: FP4 rewrites the rules of the game
A 70B parameter model requires 140GB of memory with FP16;
Switch to FP4 → only 35GB.
A desktop with 128GB unified memory can run a model that previously required 8 H100s to fit.
Accuracy loss? With QAT (Quantization-Aware Training), it's almost negligible.
3/ Key breakthrough #2: The memory wall is being broken
Is LPDDR5X bandwidth insufficient?
• Apple M4 Ultra achieves ~800 GB/s with ultra-wide bit width
• LPDDR6 (2027) doubles bandwidth again
• NVIDIA DGX Spark uses GB10 + coherent memory architecture
The desktop is no longer a "castrated GPU", but a "new species optimized for inference".
4/ Key breakthrough #3: You don't need a data center at all
Data centers solve:
✅ Training frontier models (trillion-level parameters)
✅ Serving billions of users concurrently worldwide
What individuals need:
✅ A local brain that can run 70B–200B models
✅ Privacy, low latency, no monthly fees
These two things are fundamentally different problems.
5/ Investment insights 💡
• HBM remains the king on the training side (SK Hynix, Micron)
• But edge inference chips + high-bandwidth LPDDR/unified memory will be the new battleground of the next decade
• NVIDIA DGX Spark, Apple Silicon, AMD Strix Halo, Qualcomm X Elite — all are positioning themselves
The future is not cloud vs. desktop; the cloud does training, the desktop does your AI.
Not because desktops are powerful enough to replace the cloud, but because AI's "demand structure" is splitting—
Training stays in the cloud, inference returns to the local.
2/ Key Breakthrough 1: FP4 Rewrites the Rules
A 70B parameter model requires 140GB of memory in FP16;
Switch to FP4 → only 35GB.
A desktop with 128GB unified memory can run a model that previously required 8 H100s.
Accuracy loss? It's almost negligible with QAT (Quantization-Aware Training).
3/ Key Breakthrough 2: The Memory Wall is Being Broken
LPDDR5X bandwidth insufficient?
• Apple M4 Ultra achieves ~800 GB/s with an ultra-wide bit width
• LPDDR6 (2027) doubles bandwidth again
• NVIDIA DGX Spark uses GB10 + coherent memory architecture
The desktop is no longer a "crippled GPU" but a "new species optimized for inference."
4/ Key Breakthrough 3: You Don't Need a Data Center at All
Data centers solve:
✅ Training frontier models (trillion-parameter)
✅ Serving billions of global users concurrently
What individuals need:
✅ A local brain that can run 70B–200B models
✅ Privacy, low latency, no monthly fee
These are fundamentally different problems.
5/ Investment Implications 💡
• HBM is still the king on the training side (SK Hynix, Micron)
• But edge inference chips + high-bandwidth LPDDR/unified memory will be the new battleground for the next decade
• NVIDIA DGX Spark, Apple Silicon, AMD Strix Halo, Qualcomm X Elite — all jockeying for position
The future is not cloud vs. desktop; it's cloud for training, desktop for your AI.