DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a Costs (K GPU Hours/T Tokens) 0 100 200 300 400 DeepSeek-V2 DeepSeek 67B reducing KV cache by 93.3% KV Cache for Generation (KB/Token) 0 10000 20000 30000 40000 50000 DeepSeek-V2 DeepSeek 67B 576%

Source and Upstream Master ) 。， Optimize on INT8 & FP32 AiiOS ! 驱动万物智能 Alios TVM @ ARM CPU INT8 * Cache 芍四 Data FO Data FOData … QNNPACK Convolution 。，NHWC layout Cach， 浆百 FeU + pack re 。 Tensorize GEMM Cache 大站 Fe Data FO Data … FOData QNNPACK /NiiOS ! 驱动万物智能 P Cache 浆加 Data FO Data FOData … NHWC L2 da … FL2 da Alios TVM @ ARM CPU

Invokes a Relay closure. InvokePacked Invokes a TVM compiled kernel. AllocStorage Allocates a storage block. AllocTensor Allocates a tensor value of a certain shape. AllocTensorReg Allocates a tensor based = [tvm.relay.Any(), 3, 224, 224] dtype = "float32" block = get_model('resnet50_v1', pretrained=True) mod, params = relay.frontend.from_mxnet(block, shape={input_name: input_shape}, dtype=dtype) tvm

and model co-design - PyTorch operator overhead makes interpreter infeasible - Reduce FLOPs with block-sparsified weight matrices - not a new idea, cf WaveRNN, Sparse Transformers, etc - Reduce precision Related work in Gibiansky (2017), Gray (2019), et al. Image from OpenAI- Add relay.nn.sparse_dense for block-sparse matrix multiplication (~50 lines of TVM IR) - Add relay.reinterpret to implement rational

ce 2 |sep Cooperative Fetching lets threads in the same thread block cooperatively fetch dependent data out_channel WwWly, pm Bly zx) https://docstvm ] Cooperative Fetching Lets threads (work item) in the same thread block (work group) cooperatively fetch dependent data https/www khronos.org/registry/DpenCLspecs/opencl-1

during the renaming process. It would be better to wrap the `shutil.move` call in a `try...except` block to catch any potential errors. Here is the improved code with these suggestions: ```python import

000 enterprises and digital natives – from Atomicwork, to Epic, Fujitsu, and Gainsight, to H&R Block and LG Electronics – to design, customize, and manage their AI apps and agents. We processed over