{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:RVZJ46DEQL7GYJ6HJZMKPTZ7BG","short_pith_number":"pith:RVZJ46DE","canonical_record":{"source":{"id":"2411.00136","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-31T18:34:59Z","cross_cats_sorted":[],"title_canon_sha256":"58c8afc86de19f0618c5bee80d76ad1bc1426039117e5bae6059184ccadf19e2","abstract_canon_sha256":"89d2af006f73a23978f0ef4899f6952b75badb80a187b8f71f86ea7eafe878f4"},"schema_version":"1.0"},"canonical_sha256":"8d729e786482fe6c27c74e58a7cf3f09a572495fea10de2a6ca8c2ebd5cf7aa0","source":{"kind":"arxiv","id":"2411.00136","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.00136","created_at":"2026-07-05T09:29:38Z"},{"alias_kind":"arxiv_version","alias_value":"2411.00136v1","created_at":"2026-07-05T09:29:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.00136","created_at":"2026-07-05T09:29:38Z"},{"alias_kind":"pith_short_12","alias_value":"RVZJ46DEQL7G","created_at":"2026-07-05T09:29:38Z"},{"alias_kind":"pith_short_16","alias_value":"RVZJ46DEQL7GYJ6H","created_at":"2026-07-05T09:29:38Z"},{"alias_kind":"pith_short_8","alias_value":"RVZJ46DE","created_at":"2026-07-05T09:29:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:RVZJ46DEQL7GYJ6HJZMKPTZ7BG","target":"record","payload":{"canonical_record":{"source":{"id":"2411.00136","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-31T18:34:59Z","cross_cats_sorted":[],"title_canon_sha256":"58c8afc86de19f0618c5bee80d76ad1bc1426039117e5bae6059184ccadf19e2","abstract_canon_sha256":"89d2af006f73a23978f0ef4899f6952b75badb80a187b8f71f86ea7eafe878f4"},"schema_version":"1.0"},"canonical_sha256":"8d729e786482fe6c27c74e58a7cf3f09a572495fea10de2a6ca8c2ebd5cf7aa0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:29:38.035818Z","signature_b64":"94lymdS4oRsjRnzz2kab6MPW3PiYNlasqYJ0f6TBeMh8YFncx4N+bU/pfNSuTnR4XiuDsKXR70+vUH3KkOLQCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d729e786482fe6c27c74e58a7cf3f09a572495fea10de2a6ca8c2ebd5cf7aa0","last_reissued_at":"2026-07-05T09:29:38.035337Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:29:38.035337Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.00136","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:29:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GEBBLQEuNEIAu0HbXQqdH3mFMfit9DNQ22ORA98RoXJU5H42kTeTWy3c5Jp4B2hE2mcLUEEqE401TQjMNGh8CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T18:38:17.943284Z"},"content_sha256":"55a7d850cd744efbf24e7d4abc480609d897cf7b21274c347e2ce2b4f89c3dab","schema_version":"1.0","event_id":"sha256:55a7d850cd744efbf24e7d4abc480609d897cf7b21274c347e2ce2b4f89c3dab"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:RVZJ46DEQL7GYJ6HJZMKPTZ7BG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aditya Tanikanti, Bharat Kale, Farah Ferdaus, Ken Raffenetti, Krishna Teja Chitty-Venkata, Murali Emani, Siddhisanket Raskar, Valerie Taylor, Venkatram Vishwanath","submitted_at":"2024-10-31T18:34:59Z","abstract_excerpt":"Large Language Models (LLMs) have propelled groundbreaking advancements across several domains and are commonly used for text generation applications. However, the computational demands of these complex models pose significant challenges, requiring efficient hardware acceleration. Benchmarking the performance of LLMs across diverse hardware platforms is crucial to understanding their scalability and throughput characteristics. We introduce LLM-Inference-Bench, a comprehensive benchmarking suite to evaluate the hardware inference performance of LLMs. We thoroughly analyze diverse hardware platf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.00136","kind":"arxiv","version":1},"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/2411.00136/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:29:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yrxquwVPenJT4yDVplE7naq/DY3Sj1kW8bLhLPN8ncmmk5UCJVR464FLd8T0vv7yAbT1+oOHYCnxSyo6DlfvBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T18:38:17.943792Z"},"content_sha256":"7ea2a874afdf8aa6f61d81c80cfdf7afbad623d1f845f5fc20df58f496a8959f","schema_version":"1.0","event_id":"sha256:7ea2a874afdf8aa6f61d81c80cfdf7afbad623d1f845f5fc20df58f496a8959f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RVZJ46DEQL7GYJ6HJZMKPTZ7BG/bundle.json","state_url":"https://pith.science/pith/RVZJ46DEQL7GYJ6HJZMKPTZ7BG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RVZJ46DEQL7GYJ6HJZMKPTZ7BG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-22T18:38:17Z","links":{"resolver":"https://pith.science/pith/RVZJ46DEQL7GYJ6HJZMKPTZ7BG","bundle":"https://pith.science/pith/RVZJ46DEQL7GYJ6HJZMKPTZ7BG/bundle.json","state":"https://pith.science/pith/RVZJ46DEQL7GYJ6HJZMKPTZ7BG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RVZJ46DEQL7GYJ6HJZMKPTZ7BG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:RVZJ46DEQL7GYJ6HJZMKPTZ7BG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"89d2af006f73a23978f0ef4899f6952b75badb80a187b8f71f86ea7eafe878f4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-31T18:34:59Z","title_canon_sha256":"58c8afc86de19f0618c5bee80d76ad1bc1426039117e5bae6059184ccadf19e2"},"schema_version":"1.0","source":{"id":"2411.00136","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.00136","created_at":"2026-07-05T09:29:38Z"},{"alias_kind":"arxiv_version","alias_value":"2411.00136v1","created_at":"2026-07-05T09:29:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.00136","created_at":"2026-07-05T09:29:38Z"},{"alias_kind":"pith_short_12","alias_value":"RVZJ46DEQL7G","created_at":"2026-07-05T09:29:38Z"},{"alias_kind":"pith_short_16","alias_value":"RVZJ46DEQL7GYJ6H","created_at":"2026-07-05T09:29:38Z"},{"alias_kind":"pith_short_8","alias_value":"RVZJ46DE","created_at":"2026-07-05T09:29:38Z"}],"graph_snapshots":[{"event_id":"sha256:7ea2a874afdf8aa6f61d81c80cfdf7afbad623d1f845f5fc20df58f496a8959f","target":"graph","created_at":"2026-07-05T09:29:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.00136/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) have propelled groundbreaking advancements across several domains and are commonly used for text generation applications. However, the computational demands of these complex models pose significant challenges, requiring efficient hardware acceleration. Benchmarking the performance of LLMs across diverse hardware platforms is crucial to understanding their scalability and throughput characteristics. We introduce LLM-Inference-Bench, a comprehensive benchmarking suite to evaluate the hardware inference performance of LLMs. We thoroughly analyze diverse hardware platf","authors_text":"Aditya Tanikanti, Bharat Kale, Farah Ferdaus, Ken Raffenetti, Krishna Teja Chitty-Venkata, Murali Emani, Siddhisanket Raskar, Valerie Taylor, Venkatram Vishwanath","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-31T18:34:59Z","title":"LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.00136","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:55a7d850cd744efbf24e7d4abc480609d897cf7b21274c347e2ce2b4f89c3dab","target":"record","created_at":"2026-07-05T09:29:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"89d2af006f73a23978f0ef4899f6952b75badb80a187b8f71f86ea7eafe878f4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-31T18:34:59Z","title_canon_sha256":"58c8afc86de19f0618c5bee80d76ad1bc1426039117e5bae6059184ccadf19e2"},"schema_version":"1.0","source":{"id":"2411.00136","kind":"arxiv","version":1}},"canonical_sha256":"8d729e786482fe6c27c74e58a7cf3f09a572495fea10de2a6ca8c2ebd5cf7aa0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8d729e786482fe6c27c74e58a7cf3f09a572495fea10de2a6ca8c2ebd5cf7aa0","first_computed_at":"2026-07-05T09:29:38.035337Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:29:38.035337Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"94lymdS4oRsjRnzz2kab6MPW3PiYNlasqYJ0f6TBeMh8YFncx4N+bU/pfNSuTnR4XiuDsKXR70+vUH3KkOLQCw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:29:38.035818Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.00136","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:55a7d850cd744efbf24e7d4abc480609d897cf7b21274c347e2ce2b4f89c3dab","sha256:7ea2a874afdf8aa6f61d81c80cfdf7afbad623d1f845f5fc20df58f496a8959f"],"state_sha256":"58882a26d08dcbffcfc9e0b2adaa7c3853562a627401238dca75ecc94e3bd7e2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w4dOIFrf45HZSn9JFKtxIaFVJvrYwciC/RvIZdFg8QD2kHcLtk+E3sD3p+ZQDLR3vfUXa4hLmfzNc8nTDJWwCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T18:38:17.947617Z","bundle_sha256":"44d87795e3224963d9b70e44bac4b7ff0758ef567bdd5eac2211c282b8ec85bc"}}