Hi, thanks! I use vllm to inference the llama-7B model on single gpu, and tensor-parallel on 2-gpus and 4-gpus, we found that it is 10 times faster than HF on a single GPU, but using tensor parallelism, there is no significant increase i...
$ curl http://localhost:8888/v1/models{"data":[{"id":"llama8b-instruct-awq","object":"model","owned_by":"vllm","root":"llama8b-instruct-awq", ...}]}$ curl http://localhost:8888/v1/completions\-H"Content-Type: application/json"\-d'{"model": "llama8b-instruct-awq","prom...
1. SGLang 的多 GPU 策略 张量并行(Tensor Parallelism) 模型权重沿特定维度分割到多个 GPU(如--tp 8表示 8 GPU 并行)。 数据并行(Data Parallelism) 输入数据分片处理,结合连续批处理实现负载均衡。 缓存共享 通过RadixAttention 跨 GPU 共享前缀缓存,减少冗余计算。 2. vLLM 的多 GPU 策略 张量并行 类似SGL...
trust_remote_code: Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer. tensor_parallel_size: The number of GPUs to use for distributed execution with tensor parallelism. dtype: The data type for the model weights and activations. Currently, we support `float32...
CMD}"7.2 配置 VLLM 集群节点 1：bash run_cluster.sh vllm/vllm-openai:v0.7.2192.168.1.1--head /data/model -e VLLM_HOST_IP=192.168.1.1-e NCCL_SOCKET_IFNAME=eth0 -e GLOO_SOCKET_IFNAME=eth1节点2：bash run_cluster.sh vllm/vllm-openai:v0.7.2192.168.1.1--worker /data/...
vLLM Ascend即将在昇腾平台支持vLLM多个高阶特性,如请求调度算法chunked prefill,大模型分布式并行策略Tensor Parallelism (TP)、PipelineParallelism(PP),投机解码speculative decoding等,开源社区最新加速能力平滑迁移,支持昇腾平台高性能推理。 全面的社区支持,让开发更简单 ...
Tensor parallelism and pipeline parallelism support for distributed inference Streaming outputs OpenAI-compatible API server Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron. Prefix caching support ...
kubectlapply -f- << EOFapiVersion:v1kind:Secretmetadata:name:oss-secretstringData:akId:${your-accesskey-id} # 用于访问oss的AKakSecret:${your-accesskey-secert} # 用于访问oss的SK---apiVersion:v1kind:PersistentVolumemetadata:name:llm-modellabels:alicloud-pvname:llm-modelspec:capacity:storage:30...
package_data[package_name].append(file_name) def _is_hpu() -> bool: # if VLLM_TARGET_DEVICE env var was set explicitly, skip HPU autodetection if os.getenv("VLLM_TARGET_DEVICE", None) == VLLM_TARGET_DEVICE: return VLLM_TARGET_DEVICE == "hpu" ...
package_data[package_name].append(file_name) def _is_hpu() -> bool: # if VLLM_TARGET_DEVICE env var was set explicitly, skip HPU autodetection if os.getenv("VLLM_TARGET_DEVICE", None) == VLLM_TARGET_DEVICE: return VLLM_TARGET_DEVICE == "hpu" ...