Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2407.05467

The infrastructure powering IBM's Gen AI model development

Talia Gershon·Seetharami Seelam·Brian Belgodere·Milton Bonilla·Lan Hoang·Danny Barnett·I-Hsin Chung·Apoorve Mohan·Ming-Hung Chen·Lixiang Luo·Robert Walkup·Constantinos Evangelinos·Shweta Salaria·Marc Dombrowa·Yoonho Park·Apo Kayi·Liran Schour·Alim Alim·Ali Sydney·Pavlos Maniotis·Laurent Schares·Bernard Metzler·Bengi Karacali-Akyamac·Sophia Wen·Tatsuhiro Chiba·Sunyanan Choochotkaew·Takeshi Yoshimura·Claudia Misale·Tonia Elengikal·Kevin O Connor·Zhuoran Liu·Richard Molina·Lars Schneidenbach·James Caden·Christopher Laibinis·Carlos Fonseca·Vasily Tarasov·Swaminathan Sundararaman·Frank Schmuck·Scott Guthridge·Jeremy Cohn·Marc Eshel·Paul Muench·Runyu Liu·William Pointer·Drew Wyskida·Bob Krull·Ray Rose·Brent Wolfe·William Cornejo·John Walter·Colm Malone·Clifford Perucci·Frank Franco·Nigel Hinds·Bob Calio·Pavel Druyan·Robert Kilduff·John Kienle·Connor McStay·Andrew Figueroa·Matthew Connolly·Edie Fost·Gina Roma·Jake Fonseca·Ido Levy·Michele Payne·Ryan Schenkel·Amir Malki·Lion Schneider·Aniruddha Narkhede·Shekeba Moshref·Alexandra Kisin·Olga Dodin·Bill Rippon·Henry Wrieth·John Ganci·Johnny Colino·Donna Habeger-Rose·Rakesh Pandey·Aditya Gidh·Aditya Gaur·Dennis Patterson·Samsuddin Salmani·Rambilas Varma·Rumana Rumana·Shubham Sharma·Mayank Mishra·Rameswar Panda·Aditya Prasad·Matt Stallone·Gaoyuan Zhang·Yikang Shen·David Cox·Ruchir Puri·Dakshi Agrawal·Drew Thorstensen·Joel Belog·Brent Tang·Saurabh Kumar Gupta
View original source ↗Read source document ↗Export metadata ↓

Abstract

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings.

Explore related subjects

cs.DCcs.AI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Talia Gershon, Seetharami Seelam, Brian Belgodere, Milton Bonilla, Lan Hoang, Danny Barnett, I-Hsin Chung, Apoorve Mohan, Ming-Hung Chen, Lixiang Luo, Robert Walkup, Constantinos Evangelinos, Shweta Salaria, Marc Dombrowa, Yoonho Park, Apo Kayi, Liran Schour, Alim Alim, Ali Sydney, Pavlos Maniotis, Laurent Schares, Bernard Metzler, Bengi Karacali-Akyamac, Sophia Wen, Tatsuhiro Chiba, Sunyanan Choochotkaew, Takeshi Yoshimura, Claudia Misale, Tonia Elengikal, Kevin O Connor, Zhuoran Liu, Richard Molina, Lars Schneidenbach, James Caden, Christopher Laibinis, Carlos Fonseca, Vasily Tarasov, Swaminathan Sundararaman, Frank Schmuck, Scott Guthridge, Jeremy Cohn, Marc Eshel, Paul Muench, Runyu Liu, William Pointer, Drew Wyskida, Bob Krull, Ray Rose, Brent Wolfe, William Cornejo, John Walter, Colm Malone, Clifford Perucci, Frank Franco, Nigel Hinds, Bob Calio, Pavel Druyan, Robert Kilduff, John Kienle, Connor McStay, Andrew Figueroa, Matthew Connolly, Edie Fost, Gina Roma, Jake Fonseca, Ido Levy, Michele Payne, Ryan Schenkel, Amir Malki, Lion Schneider, Aniruddha Narkhede, Shekeba Moshref, Alexandra Kisin, Olga Dodin, Bill Rippon, Henry Wrieth, John Ganci, Johnny Colino, Donna Habeger-Rose, Rakesh Pandey, Aditya Gidh, Aditya Gaur, Dennis Patterson, Samsuddin Salmani, Rambilas Varma, Rumana Rumana, Shubham Sharma, Mayank Mishra, Rameswar Panda, Aditya Prasad, Matt Stallone, Gaoyuan Zhang, Yikang Shen, David Cox, Ruchir Puri, Dakshi Agrawal, Drew Thorstensen, Joel Belog, Brent Tang, Saurabh Kumar Gupta. 2025-01-13. The infrastructure powering IBM's Gen AI model development. https://arxiv.org/abs/2407.05467

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputation

Graph Neural Networks (GNNs) have been widely adopted for their ability to compute expressive node representations in graph datasets. However, serving GNNs on large graphs is challenging due to the high communication, computation, and memory overheads of constructing and executing computation graphs, which represent information flow across large neighborhoods. Existing approximation techniques in training can mitigate the overheads but, in serving, still lead to high latency and/or accuracy loss. To this end, we propose Reforge, a system that enables low-latency GNN serving for large graphs with minimal accuracy loss through two key ideas. First, Reforge employs selective recomputation of precomputed embeddings, which allows for reusing precomputed computation subgraphs while selectively recomputing a small fraction to minimize accuracy loss. Second, we develop computation graph parallelism, which reduces communication overhead by parallelizing the creation and execution of computation graphs across machines. Our evaluation with large graph datasets and GNN models shows that Reforge significantly outperforms state-of-the-art techniques.

cs.DC↗
arXiv2026

Agentic AI Workload Characteristics

Agentic AI shifts LLM serving from isolated prompt-generation requests to stateful, multi-turn executions that repeatedly invoke the model, call tools, and grow context over time. This paper characterizes ReAct-style agents from both the LLM-serving and tool-execution perspectives using an end-to-end tracing infrastructure across reasoning and non-reasoning Gemma and Qwen configurations on five agentic benchmarks. Our study shows that agentic workloads are not simply long-prompt workloads: with effective context caching, most input tokens are reused across turns, making execution decode-dominated while increasing dependence on long-lived KV-cache state. We also find that tool use has a clear temporal structure, with agents shifting from read/explore behavior early in execution to execute/write behavior later. These results show that efficient agentic serving must jointly manage repeated model re-entry, persistent context state, and workload-dependent tool behavior.

cs.DC↗
arXiv2026

Byzantine Causal Reliable Broadcast (BCRB) with Constant-Size Message Metadata

Asynchronous Byzantine Reliable Broadcast (BRB) is a fundamental primitive that guarantees agreement and validity in distributed systems subject to Byzantine faults, but it lacks ordering guarantees. In this paper, we address Byzantine Causal Reliable Broadcast (BCRB), which builds on BRB to enforce causal message ordering. We present a novel BCRB protocol that decouples causal ordering from the BRB layer, achieving constant-size $\mathcal{O}(1)$ message metadata overhead and $\mathcal{O}(n^2)$ communication word complexity as against $\mathcal{O}(n^3)$ communication word complexity of existing protocols; here $n$ is the number of processes. We present two variants of our protocol: a cryptographic version using a threshold encryption scheme and sequence gating, and its non-cryptographic version. In the cryptographic version, senders broadcast ciphertexts immediately, and decryption shares are piggybacked on out-of-band ACKs, preventing early decryption and front-running. In both versions, causal safety is achieved probabilistically. We evaluate the probability of causal safety violations using a random variable path analysis under independent exponential link delay distributions. We show that both variants satisfy liveness and the probability of weak safety violation is bounded by $\mathcal{O}(f^{-3}\cdot\ln^3 f)$, where $f$ is the upper bound on the number of Byzantine processes, and $f < n/3$ and $f=\mathcal{O}(n)$. Further, for the crypto version, we show that the probability of strong safety violation is bounded by $\mathcal{O}(f^{-1} \cdot \ln^2 f)$. We also show how to modify our two protocols to guarantee 100\% weak safety keeping $\mathcal{O}(1)$ message space overhead but with $\mathcal{O}(n^3)$ messages and $\mathcal{O}(n^3)$ communication word complexity.

cs.DC↗