{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CCST2BYOK2VLOMZ6PSNGPGO3K3","short_pith_number":"pith:CCST2BYO","schema_version":"1.0","canonical_sha256":"10a53d070e56aab7333e7c9a6799db56d6da67118df67d44173927a85f5c65d5","source":{"kind":"arxiv","id":"2410.11199","version":2},"attestation_state":"computed","paper":{"title":"Isambard-AI: a leadership class supercomputer optimised specifically for Artificial Intelligence","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DC","authors_text":"Christopher Woods, Sadaf R Alam, Simon McIntosh-Smith","submitted_at":"2024-10-15T02:34:26Z","abstract_excerpt":"Isambard-AI is a new, leadership-class supercomputer, designed to support AI-related research. Based on the HPE Cray EX4000 system, and housed in a new, energy efficient Modular Data Centre in Bristol, UK, Isambard-AI employs 5,448 NVIDIA Grace-Hopper GPUs to deliver over 21 ExaFLOP/s of 8-bit floating point performance for LLM training, and over 250 PetaFLOP/s of 64-bit performance, for under 5MW. Isambard-AI integrates two, all-flash storage systems: a 20 PiByte Cray ClusterStor and a 3.5 PiByte VAST solution. Combined these give Isambard-AI flexibility for training, inference and secure dat"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.11199","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2024-10-15T02:34:26Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c0f1673365e7b7646a770626645a824f1e8b6a7c831a4046eb223b181d9b3a4d","abstract_canon_sha256":"eb8a3e2d583dd09c93963cc368a8c234dc7bfcd290f9ea1b62cdfaa6255412a2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:30:22.226597Z","signature_b64":"ma950MH2tLEhg7QCLrtZjT8Q0blE9ND1YfOHTTkohSCFfCV3579dZ2KgU5UVnpitsxD8inEk5mdJGSADdXERCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10a53d070e56aab7333e7c9a6799db56d6da67118df67d44173927a85f5c65d5","last_reissued_at":"2026-07-05T09:30:22.226158Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:30:22.226158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Isambard-AI: a leadership class supercomputer optimised specifically for Artificial Intelligence","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DC","authors_text":"Christopher Woods, Sadaf R Alam, Simon McIntosh-Smith","submitted_at":"2024-10-15T02:34:26Z","abstract_excerpt":"Isambard-AI is a new, leadership-class supercomputer, designed to support AI-related research. Based on the HPE Cray EX4000 system, and housed in a new, energy efficient Modular Data Centre in Bristol, UK, Isambard-AI employs 5,448 NVIDIA Grace-Hopper GPUs to deliver over 21 ExaFLOP/s of 8-bit floating point performance for LLM training, and over 250 PetaFLOP/s of 64-bit performance, for under 5MW. Isambard-AI integrates two, all-flash storage systems: a 20 PiByte Cray ClusterStor and a 3.5 PiByte VAST solution. Combined these give Isambard-AI flexibility for training, inference and secure dat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.11199","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.11199/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.11199","created_at":"2026-07-05T09:30:22.226218+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.11199v2","created_at":"2026-07-05T09:30:22.226218+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.11199","created_at":"2026-07-05T09:30:22.226218+00:00"},{"alias_kind":"pith_short_12","alias_value":"CCST2BYOK2VL","created_at":"2026-07-05T09:30:22.226218+00:00"},{"alias_kind":"pith_short_16","alias_value":"CCST2BYOK2VLOMZ6","created_at":"2026-07-05T09:30:22.226218+00:00"},{"alias_kind":"pith_short_8","alias_value":"CCST2BYO","created_at":"2026-07-05T09:30:22.226218+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":10,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.05325","citing_title":"CenSynCMB: Centre Maps and Physics-Guided Synthesis for Microbleed Detection","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2606.19460","citing_title":"Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers","ref_index":166,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12997","citing_title":"Reliability of Probabilistic Emulation of Physical Systems","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15190","citing_title":"RAVEN: Real-time Autoregressive Video Extrapolation with Consistency-model GRPO","ref_index":123,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12704","citing_title":"Fine-tuning MLIP foundation models: strategies for accuracy and transferability","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22444","citing_title":"Normalizing flows for all-orders QED corrections in lattice field theory","ref_index":63,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17087","citing_title":"The Learnability Gap in Medical Latent Diffusion","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18313","citing_title":"Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18419","citing_title":"Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2601.22228","citing_title":"Lost in Space? Vision-Language Models Struggle with Relative Camera Pose Estimation","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CCST2BYOK2VLOMZ6PSNGPGO3K3","json":"https://pith.science/pith/CCST2BYOK2VLOMZ6PSNGPGO3K3.json","graph_json":"https://pith.science/api/pith-number/CCST2BYOK2VLOMZ6PSNGPGO3K3/graph.json","events_json":"https://pith.science/api/pith-number/CCST2BYOK2VLOMZ6PSNGPGO3K3/events.json","paper":"https://pith.science/paper/CCST2BYO"},"agent_actions":{"view_html":"https://pith.science/pith/CCST2BYOK2VLOMZ6PSNGPGO3K3","download_json":"https://pith.science/pith/CCST2BYOK2VLOMZ6PSNGPGO3K3.json","view_paper":"https://pith.science/paper/CCST2BYO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.11199&json=true","fetch_graph":"https://pith.science/api/pith-number/CCST2BYOK2VLOMZ6PSNGPGO3K3/graph.json","fetch_events":"https://pith.science/api/pith-number/CCST2BYOK2VLOMZ6PSNGPGO3K3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CCST2BYOK2VLOMZ6PSNGPGO3K3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CCST2BYOK2VLOMZ6PSNGPGO3K3/action/storage_attestation","attest_author":"https://pith.science/pith/CCST2BYOK2VLOMZ6PSNGPGO3K3/action/author_attestation","sign_citation":"https://pith.science/pith/CCST2BYOK2VLOMZ6PSNGPGO3K3/action/citation_signature","submit_replication":"https://pith.science/pith/CCST2BYOK2VLOMZ6PSNGPGO3K3/action/replication_record"}},"created_at":"2026-07-05T09:30:22.226218+00:00","updated_at":"2026-07-05T09:30:22.226218+00:00"}