newsReddit r/LocalLLaMATrust 52 · CommunityPublished 1mo agoLive · 1mo ago
Ultra budget 20GB vram with 448GB/s for $100 bucks.
Here is the upper limit of what can be done with $100 bucks worth of video cards. You can have 3 concurrent users with plenty of context, better speeds or close enough speeds than a bunch of cards that provide less VRAM and cost 4+ times. 0.00.008.388 I log_info: verbosity = 3 (adjust with the `-lv N` CLI arg) 0.00.008.391 I device_info: 0.00.089.439 I - CUDA0 : NVIDIA P102-100 (10144 MiB, 10013 MiB free) 0.00.197.645 I - CUDA1 : NVIDIA P1
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 55%Efficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUs →
- PossiblePossibly related (embedding) · 49%GPU Parallelization Strategies for Forward and Backward Propagation in Shallow Neural Networks: A CUDA-Based Comparative Study →
- PossiblePossibly related (embedding) · 49%NVIDIA-NeMo/Curator →
- PossiblePossibly related (embedding) · 46%kekzl/imp →
- PossiblePossibly related (embedding) · 45%NVIDIA/DALI →
- PossiblePossibly related (embedding) · 50%DaoyuanLi2816/llm-gpu-lab →
- PossiblePossibly related (embedding) · 53%nibor1896/Crow →
- PossiblePossibly related (embedding) · 50%NVIDIA/nvImageCodec →
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paperEfficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUspaperGPU Parallelization Strategies for Forward and Backward Propagation in Shallow Neural Networks: A CUDA-Based Comparative StudyrepoNVIDIA-NeMo/Curatorrepokekzl/imprepoNVIDIA/DALIrepoDaoyuanLi2816/llm-gpu-lab
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Related across the graph
paperEfficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUsrepoNVIDIA/DALIrepoNVIDIA/nvImageCodecrepoDaoyuanLi2816/llm-gpu-labreponibor1896/CrowpaperGPU Parallelization Strategies for Forward and Backward Propagation in Shallow Neural Networks: A CUDA-Based Comparative Studyrepokekzl/imprepoovg-project/kvcachedrepoNVIDIA-NeMo/Curator