Crypto Flexs
  • DIRECTORY
  • CRYPTO
    • ETHEREUM
    • BITCOIN
    • ALTCOIN
  • BLOCKCHAIN
  • EXCHANGE
  • TRADING
  • SUBMIT
Crypto Flexs
  • DIRECTORY
  • CRYPTO
    • ETHEREUM
    • BITCOIN
    • ALTCOIN
  • BLOCKCHAIN
  • EXCHANGE
  • TRADING
  • SUBMIT
Crypto Flexs
Home»ADOPTION NEWS»NVIDIA NVLink and NVSwitch Improve Large-Scale Language Model Inference
ADOPTION NEWS

NVIDIA NVLink and NVSwitch Improve Large-Scale Language Model Inference

By Crypto FlexsAugust 13, 20243 Mins Read
Facebook Twitter Pinterest LinkedIn Tumblr Email
NVIDIA NVLink and NVSwitch Improve Large-Scale Language Model Inference
Share
Facebook Twitter LinkedIn Pinterest Email

Felix Pinkston
13 Aug 2024 07:49

NVIDIA’s NVLink and NVSwitch technologies enhance large-scale language model inference, enabling faster and more efficient multi-GPU processing.





As large-scale language models (LLMs) rapidly scale, the computing power needed to handle inference requests increases. According to the NVIDIA Technology Blog, multi-GPU computing is essential to meet real-time latency requirements and serve a growing number of users.

Benefits of Multi-GPU Computing

Even if a large model fits into a single memory of a modern GPU, the speed at which tokens are generated depends on the total compute power available. Combining the capabilities of multiple cutting-edge GPUs enables real-time user experiences. Technologies such as Tensor Parallelism (TP) can be used to speed up inference requests and carefully select the number of GPUs for each model to optimize user experience and cost.

Multi-GPU Inference: Communication Intensive

Multi-GPU TP inference involves distributing the computation of each model layer across multiple GPUs. The GPUs must communicate extensively to share results to advance to the next model layer. This communication is important because the Tensor Cores are often idle, waiting for data. For example, a single query on Llama 3.1 70B can require up to 20 GB of data transfer per GPU, highlighting the need for a high-bandwidth interconnect.

NVSwitch: The Key to Fast Multi-GPU LLM Inference

Effective multi-GPU scaling requires GPUs with high interconnect bandwidth per GPU and fast connectivity. NVIDIA Hopper Architecture GPUs with fourth-generation NVLink can communicate at 900 GB/s. When combined with NVSwitch, all GPUs in a server can communicate at this speed simultaneously, ensuring non-blocking communication. Systems such as the NVIDIA HGX H100 and H200 with multiple NVSwitch chips provide significant bandwidth, improving overall performance.

Performance comparison

Without NVSwitch, GPUs have to split their bandwidth across multiple point-to-point connections, and the communication speed decreases as more GPUs are involved. For example, point-to-point architectures only provide 128 GB/s of bandwidth for two GPUs, while NVSwitch provides 900 GB/s. This difference has a significant impact on overall inference throughput and user experience. The table in the original blog shows the bandwidth and throughput advantages of NVSwitch over point-to-point connections.

Future Innovation

NVIDIA continues to push the boundaries of real-time inference performance with NVLink and NVSwitch technology. The upcoming NVIDIA Blackwell architecture features fifth-generation NVLink, doubling the speed to 1,800 GB/s. Additionally, new NVSwitch chips and NVLink switch trays support larger NVLink domains, further improving performance on trillion-parameter models.

The NVIDIA GB200 NVL72 system, which combines 36 NVIDIA Grace CPUs and 72 NVIDIA Blackwell GPUs, is a good example of this advancement. It allows all 72 GPUs to operate as a single device, achieving real-time trillion-parameter inference that is 30x faster than the previous generation.

Image source: Shutterstock


Share. Facebook Twitter Pinterest LinkedIn Tumblr Email

Related Posts

BTC RSI hits April low as Coinbase premium turns red.

October 18, 2025

Crypto Exchange Rollish is expanded to 20 by NY approved.

October 2, 2025

SOL Leverage Longs Jump Ship, is it $ 200 next?

September 24, 2025
Add A Comment

Comments are closed.

Recent Posts

Limitless Prediction Market Closes $10M Seed Round Ahead Of LMTS Token Launch

October 20, 2025

Whale.io Introduces Crock Dentist Game And Exclusive RWA NFT Collection

October 20, 2025

Bybit Card Honored As “the Best Performing Crypto Card” By Mastercard At EDGE 2025

October 20, 2025

Jupiter Launches Ultra V3 – The Ultimate Trading Engine For Solana

October 20, 2025

Jiuzi Holdings, Inc Enters Strategic Partnership With BitFi To Advance Bitcoin-Centric Finance

October 20, 2025

DOGE And SOL Join Forces To Mine $5,997 Per Day, Making It Easy To Seize Bitcoin Wealth Together

October 20, 2025

US Bitcoin ETF loses $1.2 billion weekly

October 20, 2025

DAOs are redefining corporations, but the law is not yet ready.

October 20, 2025

BitDCA Staking Agreement Audit Summary

October 19, 2025

ETFs and liquidity drive outlook for 2026

October 19, 2025

5 Best Crypto Flash Crash And Buy The Dip Crypto Bots (2025)

October 18, 2025

Crypto Flexs is a Professional Cryptocurrency News Platform. Here we will provide you only interesting content, which you will like very much. We’re dedicated to providing you the best of Cryptocurrency. We hope you enjoy our Cryptocurrency News as much as we enjoy offering them to you.

Contact Us : Partner(@)Cryptoflexs.com

Top Insights

Limitless Prediction Market Closes $10M Seed Round Ahead Of LMTS Token Launch

October 20, 2025

Whale.io Introduces Crock Dentist Game And Exclusive RWA NFT Collection

October 20, 2025

Bybit Card Honored As “the Best Performing Crypto Card” By Mastercard At EDGE 2025

October 20, 2025
Most Popular

ARK Investment secures momentum for 21-week Bitcoin ETF bidding.

November 25, 2023

Ethereum’s double whammy: price collapse, exchange supply depletion

June 20, 2024

🏆 Altcoin Selection – January 2024 (Microcap)

January 3, 2024
  • Home
  • About Us
  • Contact Us
  • Disclaimer
  • Privacy Policy
  • Terms and Conditions
© 2025 Crypto Flexs

Type above and press Enter to search. Press Esc to cancel.