[ { "path": "table_paper/2407.00010v1.json", "table_id": "1", "section": "5.1", "all_context": [ "The systems we profile are shown in Table 1 .", "We consider these systems as they demonstrate three prominent CPU manufactures and different generations of GPUs.", "We utilize PyTorch v2.0.1, Torchvision v0.15.2, Numpy v1.26.0, Huggingface v0.20.2, and Accelerate v0.26.1.", "We note that the M1-Pro results only include the Llama-2 (7B) and Mistral (7B) results, as Falcon (7B) generally did not complete tasks in less than two orders of magnitude greater runtime.", "" ], "target_context_ids": [ 0, 1, 3 ], "selected_paragraphs": [ "[paragraph id = 0] The systems we profile are shown in Table 1 .", "[paragraph id = 1] We consider these systems as they demonstrate three prominent CPU manufactures and different generations of GPUs.", "[paragraph id = 3] We note that the M1-Pro results only include the Llama-2 (7B) and Mistral (7B) results, as Falcon (7B) generally did not complete tasks in less than two orders of magnitude greater runtime." ], "table_html": "
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System NameCPUGPU(s) per NodeDRAM per NodeVRAM per GPU
Macbook Pro10-core M1 Pro14-core M1 Pro32GB-
Swing AMD+A100264-core AMD EPYC 77428NVIDIA A1001TB40GB
Palmetto Intel+V10040-Core Intel Xeon 6148G2NVIDIA V100376GB16GB
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Table 1. Our System Configurations
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", "perturb_sentence_id": 3, "output": { "perturbed_statement": "[paragraph id = 3] We note that the M1-Pro results only include the Falcon (7B) and Mistral (7B) results, as Llama-2 (7B) generally did not complete tasks in less than two orders of magnitude greater runtime.", "perturbed_explanation": "1. The original explanation mentions that the M1-Pro results include the Llama-2 (7B) and Mistral (7B) results, whilst Falcon (7B) was excluded due to runtime inefficiency. 2. The statement is incorrect as it claims Falcon (7B) and Mistral (7B) results are included instead of the actual Llama-2 (7B) and Mistral (7B). Falcon (7B) was noted for its omission due to runtime issues, which does not align with the claim made in the statement." } }, { "path": "table_paper/2407.00010v1.json", "table_id": "1", "section": "5.3", "all_context": [ "Here, we present the impacts on runtime, energy consumption per token, and throughput for LLMs across different hardware configurations while varying the number of input tokens.", "We perform these experiments using the suite of systems outlined in Table 1 with the models outlined in Section 4.1 .", "In our experiments on the Palmetto Intel+V100 system, the V100 GPU had an out-of-memory error beyond 1024 output tokens for Falcon (7B).", "Our runtime measurements show a significant increase as input tokens grow.", "As depicted in Figure 1(a) , all systems exhibit a nonlinear escalation in runtime with increasing token counts, with the M1-Pro system showing the most significant magnitude.", "This trend highlights the computational burden imposed by larger input sizes, particularly on smaller systems that are not as well designed to handle extensive workloads.", "For all systems, we notice that throughput follows a ”roofline model” with increasing input tokens (roofline, ).", "Figure 1(b) illustrates these dynamics, indicating an increase in throughput for all systems until a certain point where inference becomes bound by compute and not by the overhead of the software, as described by roofline performance models (roofline, ).", "Energy efficiency varies markedly across different systems.", "The M1-Pro demonstrates consistently low energy consumption per token, particularly for smaller input sizes, as shown in Figure 1(c) .", "This efficiency reflects the M1-Pro s design optimization for low-power operations.", "In contrast, the Swing AMD+A100, while capable of handling more significant token inputs more efficiently, consumed more energy per token for small workloads yet became more energy efficient at larger input token sizes, underscoring a trade-off between workload size and energy efficiency.", "" ], "target_context_ids": [ 1 ], "selected_paragraphs": [ "[paragraph id = 1] We perform these experiments using the suite of systems outlined in Table 1 with the models outlined in Section 4.1 ." ], "table_html": "
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System NameCPUGPU(s) per NodeDRAM per NodeVRAM per GPU
Macbook Pro10-core M1 Pro14-core M1 Pro32GB-
Swing AMD+A100264-core AMD EPYC 77428NVIDIA A1001TB40GB
Palmetto Intel+V10040-Core Intel Xeon 6148G2NVIDIA V100376GB16GB
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Table 1. Our System Configurations
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", "perturb_sentence_id": 1, "output": { "perturbed_statement": "[paragraph id = 1] We perform these experiments using the suite of systems outlined in Table 1 with the models outlined in Section 4.1 .In our experiments on the Palmetto Intel+V100 system, the V100 GPU had an out-of-memory error beyond 512 output tokens for Falcon (7B).", "perturbed_explanation": "The original explanation: The V100 GPU experienced memory allocation issues when generating output exceeding the specified token limit (1024 tokens). 1. However, the statement mentions 512 output tokens as the limit, which contradicts the actual tested token capacity of the V100 GPU for this model. Hence, this difference establishes the inaccuracy of the statement's claim." } } ]