awdemos · February 1, 2025 18:53 · Feb 1, 2025 · Feb 1, 2025 · Feb 1, 2025
diff --git a/How many GPUs needed for GPT-3.md b/How many GPUs needed for GPT-3.md
@@ -26,33 +26,61 @@ While 10 GPUs exceed the minimum requirement, this provides headroom for larger
 
 Citations:
 [1] https://ai.stackexchange.com/questions/22877/how-much-computing-power-does-it-cost-to-run-gpt-3
+
 [2] https://news.ycombinator.com/item?id=33881504
+
 [3] https://hackernoon.com/a-deep-dive-into-how-many-gpus-it-takes-to-run-chatgpt
+
 [4] https://www.reddit.com/r/OpenAI/comments/10aocxc/does_anyone_have_any_hard_numbers_on_the_gpu/
+
 [5] https://llmgpuhelper.com/en/blog/optimizing-gpt3-multi-gpu-training
+
 [6] https://developer.nvidia.com/blog/scaling-language-model-training-to-a-trillion-parameters-using-megatron/
+
 [7] https://lambdalabs.com/blog/demystifying-gpt-3
+
 [8] https://ai.gopubby.com/multi-gpu-model-training-made-easy-with-distributed-data-parallel-ddp-453ba9f6846e?gi=a737dc56a3e4
 [9] https://blog.spheron.network/how-much-gpu-memory-is-required-to-run-a-large-language-model-find-out-here
+
 [10] https://developer.nvidia.com/blog/openai-presents-gpt-3-a-175-billion-parameters-language-model/
+
 [11] https://developer.nvidia.com/blog/deploying-a-1-3b-gpt-3-model-with-nvidia-nemo-megatron/
+
 [12] https://news.ycombinator.com/item?id=37674913
+
 [13] https://blogs.oracle.com/research/post/oracle-first-to-finetune-gpt3-sized-ai-models-with-nvidia-a100-gpu
+
 [14] https://en.wikipedia.org/wiki/GPT-3
+
 [15] https://www.reddit.com/r/nvidia/comments/113euip/openai_trained_chat_gpt_on_10k_a100s/
+
 [16] https://www.fierceelectronics.com/sensors/chatgpt-runs-10k-nvidia-training-gpus-potential-thousands-more
+
 [17] https://www.lesswrong.com/posts/HBisQEDajGwhWirky/how-feasible-costly-would-it-be-to-train-a-very-large-ai
+
 [18] https://developer.nvidia.com/blog/efficiently-scale-llm-training-across-a-large-gpu-cluster-with-alpa-and-ray/
+
 [19] https://lambdalabs.com/blog/demystifying-gpt-3
+
 [20] https://www.reddit.com/r/singularity/comments/inp025/if_you_want_to_run_your_own_full_gpt3_instance/
 [21] https://techcommunity.microsoft.com/blog/machinelearningblog/unlocking-the-power-of-large-scale-training-in-ai/4303390
+
 [22] https://hackernoon.com/a-deep-dive-into-how-many-gpus-it-takes-to-run-chatgpt
+
 [23] https://rethinkpriorities.org/research-area/gpt-3-like-models-are-now-much-easier-to-access-and-deploy-than-to-develop/
+
 [24] https://company.hpc-ai.com/blog/train-18-billion-parameter-gpt-models-with-a-single-gpu-on-your-personal-computer
-[25] https://arstechnica.com/civis/threads/you-can-now-run-a-gpt-3-level-ai-model-on-your-laptop-phone-and-raspberry-pi.1490659/page-2
+
+[25] https://arstechnica.com/civis/threads/you-can-now-run-a-gpt-3-level-ai-model-on-your-laptop-phone-and-raspberrypi.1490659/page-2
+
 [26] https://www.weka.io/blog/gpu/gpu-for-ai/
+
 [27] https://www.reddit.com/r/MachineLearning/comments/gzb5uv/d_what_would_it_take_to_run_openais_gpt3_on/
+
 [28] https://www.reddit.com/r/OpenAI/comments/11fwfjg/what_kind_of_pc_would_you_need_to_have_a_model/
+
 [29] https://www.reddit.com/r/GPT3/comments/zufeg9/how_long_before_we_can_run_gpt3_locally/
+
 [30] https://rethinkpriorities.org/research-area/the-replication-and-emulation-of-gpt-3/
+
 [31] https://arxiv.org/pdf/2104.04473.pdf
diff --git a/gistfile1.txt → How many GPUs needed for GPT-3.md b/gistfile1.txt → How many GPUs needed for GPT-3.md
diff --git a/gistfile1.txt b/gistfile1.txt
@@ -0,0 +1,58 @@
+GPT-3's computational requirements depend heavily on model size, precision, and hardware optimization. For the full 175B-parameter model, **running inference requires at least 5 NVIDIA A100 GPUs (80GB each)** to meet the 350GB memory requirement when using FP16 precision[4][9][22]. However, practical implementations often use 8 GPUs for improved parallelism and throughput[3][22]. 
+
+### Key Technical Requirements:
+- **Memory**:  
+  - 175B parameters require **350GB of VRAM** at FP16 precision (2 bytes/parameter)[4][9][14].  
+  - Consumer GPUs like RTX 3090s (24GB VRAM) can technically work in multi-GPU setups (e.g., 8x24GB = 192GB), but require aggressive memory optimizations like 8-bit quantization[2][28].  
+
+- **Hardware Recommendations**:  
+  - **Data center GPUs**: 5–8 NVIDIA A100/A800 (80GB) GPUs for stable deployment[3][4][13].  
+  - **Consumer GPUs**: 8x RTX 3090/4090 with PCIe 5.0 and NVLink for reduced communication bottlenecks[2][28].  
+
+### Performance Considerations:
+- **Inference speed**:  
+  - A single A100 generates ~1 word every 350ms for GPT-3[3].  
+  - An 8-GPU cluster achieves **15–20 words/sec** with batch size 1[3][22].  
+- **Cost**:  
+  - Cloud deployment costs ~$6–7/hour for 8xA100 instances[2].  
+  - On-prem setups with 8xRTX 3090s cost ~$10K for hardware[2].  
+
+### Optimization Techniques:
+1. **Model Parallelism**: Split layers across GPUs to overcome memory limits[5][6].  
+2. **Quantization**: 8-bit weights reduce memory usage to ~1 byte/parameter[2][9].  
+3. **KV Caching**: Reuse attention computations to reduce redundant calculations[9].  
+
+While 10 GPUs exceed the minimum requirement, this provides headroom for larger batch sizes or hybrid training/inference workloads. Oracle demonstrated GPT-3-sized model inference with 8xA100 GPUs[13], confirming feasibility for high-end deployments.
+
+Citations:
+[1] https://ai.stackexchange.com/questions/22877/how-much-computing-power-does-it-cost-to-run-gpt-3
+[2] https://news.ycombinator.com/item?id=33881504
+[3] https://hackernoon.com/a-deep-dive-into-how-many-gpus-it-takes-to-run-chatgpt
+[4] https://www.reddit.com/r/OpenAI/comments/10aocxc/does_anyone_have_any_hard_numbers_on_the_gpu/
+[5] https://llmgpuhelper.com/en/blog/optimizing-gpt3-multi-gpu-training
+[6] https://developer.nvidia.com/blog/scaling-language-model-training-to-a-trillion-parameters-using-megatron/
+[7] https://lambdalabs.com/blog/demystifying-gpt-3
+[8] https://ai.gopubby.com/multi-gpu-model-training-made-easy-with-distributed-data-parallel-ddp-453ba9f6846e?gi=a737dc56a3e4
+[9] https://blog.spheron.network/how-much-gpu-memory-is-required-to-run-a-large-language-model-find-out-here
+[10] https://developer.nvidia.com/blog/openai-presents-gpt-3-a-175-billion-parameters-language-model/
+[11] https://developer.nvidia.com/blog/deploying-a-1-3b-gpt-3-model-with-nvidia-nemo-megatron/
+[12] https://news.ycombinator.com/item?id=37674913
+[13] https://blogs.oracle.com/research/post/oracle-first-to-finetune-gpt3-sized-ai-models-with-nvidia-a100-gpu
+[14] https://en.wikipedia.org/wiki/GPT-3
+[15] https://www.reddit.com/r/nvidia/comments/113euip/openai_trained_chat_gpt_on_10k_a100s/
+[16] https://www.fierceelectronics.com/sensors/chatgpt-runs-10k-nvidia-training-gpus-potential-thousands-more
+[17] https://www.lesswrong.com/posts/HBisQEDajGwhWirky/how-feasible-costly-would-it-be-to-train-a-very-large-ai
+[18] https://developer.nvidia.com/blog/efficiently-scale-llm-training-across-a-large-gpu-cluster-with-alpa-and-ray/
+[19] https://lambdalabs.com/blog/demystifying-gpt-3
+[20] https://www.reddit.com/r/singularity/comments/inp025/if_you_want_to_run_your_own_full_gpt3_instance/
+[21] https://techcommunity.microsoft.com/blog/machinelearningblog/unlocking-the-power-of-large-scale-training-in-ai/4303390
+[22] https://hackernoon.com/a-deep-dive-into-how-many-gpus-it-takes-to-run-chatgpt
+[23] https://rethinkpriorities.org/research-area/gpt-3-like-models-are-now-much-easier-to-access-and-deploy-than-to-develop/
+[24] https://company.hpc-ai.com/blog/train-18-billion-parameter-gpt-models-with-a-single-gpu-on-your-personal-computer
+[25] https://arstechnica.com/civis/threads/you-can-now-run-a-gpt-3-level-ai-model-on-your-laptop-phone-and-raspberry-pi.1490659/page-2
+[26] https://www.weka.io/blog/gpu/gpu-for-ai/
+[27] https://www.reddit.com/r/MachineLearning/comments/gzb5uv/d_what_would_it_take_to_run_openais_gpt3_on/
+[28] https://www.reddit.com/r/OpenAI/comments/11fwfjg/what_kind_of_pc_would_you_need_to_have_a_model/
+[29] https://www.reddit.com/r/GPT3/comments/zufeg9/how_long_before_we_can_run_gpt3_locally/
+[30] https://rethinkpriorities.org/research-area/the-replication-and-emulation-of-gpt-3/
+[31] https://arxiv.org/pdf/2104.04473.pdf