{"id":92086,"date":"2026-08-09T09:41:00","date_gmt":"2026-08-09T01:41:00","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/92086.html"},"modified":"2026-08-09T09:41:00","modified_gmt":"2026-08-09T01:41:00","slug":"2026%e5%b9%b4llm%e6%8e%a8%e7%90%86%e6%9c%8d%e5%8a%a1%e5%99%a8%e7%94%9f%e4%ba%a7%e7%ba%a7%e5%af%b9%e6%af%94%ef%bc%9avllm%e3%80%81sglang%e3%80%81tensorrt-llm%e3%80%81lmdeploy%e4%b8%8etgi%e7%9a%84","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/92086.html","title":{"rendered":"2026\u5e74LLM\u63a8\u7406\u670d\u52a1\u5668\u751f\u4ea7\u7ea7\u5bf9\u6bd4\uff1avLLM\u3001SGLang\u3001TensorRT-LLM\u3001LMDeploy\u4e0eTGI\u7684\u5de5\u7a0b\u9009\u578b"},"content":{"rendered":"

2026\u5e746\u6708，LLM\u63a8\u7406\u670d\u52a1\u5668\u5df2\u7ecf\u4ece"\u767e\u82b1\u9f50\u653e"\u8d70\u5411"\u56db\u5f3a\u4e89\u9738"\u3002vLLM\u3001SGLang\u3001TensorRT-LLM\u3001LMDeploy\u3001Text Generation Inference (TGI) \u662f\u5f53\u524d\u6700\u4e3b\u6d41\u7684\u4e94\u4e2a\u9009\u62e9\u3002\u672c\u6587\u4ece\u6027\u80fd\u3001\u7279\u6027\u3001\u7a33\u5b9a\u6027\u3001\u751f\u6001\u56db\u4e2a\u7ef4\u5ea6\u505a\u7cfb\u7edf\u5bf9\u6bd4，\u5e76\u7ed9\u51fa\u57fa\u4e8e\u4e1a\u52a1\u573a\u666f\u7684\u9009\u578b\u5efa\u8bae\u3002<\/p>\n

\u4e00\u3001\u4e94\u5927\u63a8\u7406\u670d\u52a1\u5668\u5168\u666f### 1.1 vLLM（UC Berkeley\u5f00\u6e90）\u5b9a\u4f4d：\u901a\u7528LLM\u63a8\u7406\u7684\u4e8b\u5b9e\u6807\u51c6\u3002\u6838\u5fc3\u7279\u6027：- PagedAttention：KV Cache\u5206\u9875\u7ba1\u7406，\u541e\u5410\u91cf4-24\u500d\u63d0\u5347- Continuous Batching：\u52a8\u6001\u5408\u5e76\u8bf7\u6c42，\u6700\u5927\u5316GPU\u5229\u7528\u7387- \u591a\u6a21\u578b\u652f\u6301：Llama\u3001Qwen\u3001Mistral\u3001DeepSeek\u7b49\u4e3b\u6d41\u67b6\u6784- \u5206\u5e03\u5f0f\u63a8\u7406：Tensor Parallel\u3001Pipeline Parallel- OpenAI\u517c\u5bb9API：\u4e00\u884c\u5207\u6362OpenAI\u5ba2\u6237\u7aef2026\u5e74\u72b6\u6001：v0.6+\u7248\u672c，\u5df2\u652f\u6301Speculative Decoding\u3001Prefix Caching\u3001\u591a\u6a21\u6001\u3002### 1.2 SGLang（UC Berkeley + LMSYS）\u5b9a\u4f4d：\u7ed3\u6784\u5316\u751f\u6210\u4e0eAgent\u63a8\u7406\u7684\u9ad8\u6027\u80fdRuntime\u3002\u6838\u5fc3\u7279\u6027：- RadixAttention：\u57fa\u4e8eRadix Tree\u7684Prefix Caching，3-10\u500d\u541e\u5410\u63d0\u5347- \u7ed3\u6784\u5316\u751f\u6210：\u539f\u751f\u652f\u6301JSON Schema\u3001Grammar\u3001Tool Call- \u524d\u7aef\u8bed\u8a00：\u7c7b\u4f3cPython DSL，\u7b80\u5316Agent\u7f16\u7a0b- \u591a\u6a21\u6001：\u539f\u751f\u652f\u6301\u89c6\u89c9\u6a21\u578b2026\u5e74\u72b6\u6001：v0.3+\u7248\u672c，\u5df2\u6210\u4e3aLMSYS Chatbot Arena\u7684\u9ed8\u8ba4\u540e\u7aef\u3002### 1.3 TensorRT-LLM（NVIDIA\u5b98\u65b9）\u5b9a\u4f4d：NVIDIA\u751f\u6001\u7684\u6027\u80fd\u6781\u81f4\u3002\u6838\u5fc3\u7279\u6027：- \u6781\u81f4\u6027\u80fd：\u9488\u5bf9Hopper\/Blackwell\u67b6\u6784\u6df1\u5ea6\u4f18\u5316- In-flight Batching：\u7c7b\u4f3cContinuous Batching\u4f46\u66f4\u7cbe\u7ec6- Quantization：FP8\u3001INT4\u3001INT8\u539f\u751f\u652f\u6301- Multi-GPU：Tensor Parallel\u3001Pipeline Parallel\u6210\u719f- \u751f\u6001\u5b8c\u6574：\u4e0eTriton Inference Server\u65e0\u7f1d\u96c6\u62102026\u5e74\u72b6\u6001：v0.10+，\u652f\u6301Llama 4\u3001Qwen3\u3001Mistral Large 2\u3002### 1.4 LMDeploy（\u4e0a\u6d77\u4eba\u5de5\u667a\u80fd\u5b9e\u9a8c\u5ba4）\u5b9a\u4f4d：\u56fd\u4ea7\u6a21\u578b\u63a8\u7406\u7684\u9ad8\u6027\u80fd\u5f15\u64ce\u3002\u6838\u5fc3\u7279\u6027：- Turbomind：\u81ea\u7814\u63a8\u7406\u5f15\u64ce，\u56fd\u4ea7GPU\/CPU\u5168\u9762\u652f\u6301- Persistent Batch：\u51cf\u5c11\u8bf7\u6c42\u8c03\u5ea6\u5f00\u9500- \u91cf\u5316\u652f\u6301：AWQ\u3001GPTQ\u3001BNB\u539f\u751f- \u56fd\u4ea7\u786c\u4ef6：\u652f\u6301\u534e\u4e3a\u6607\u817e\u3001\u5bd2\u6b66\u7eaa\u3001\u6d77\u5149DCU2026\u5e74\u72b6\u6001：v0.5+\u7248\u672c，InternLM\u3001Qwen\u3001DeepSeek\u5b98\u65b9\u63a8\u8350\u3002### 1.5 TGI（Text Generation Inference，HuggingFace\u5b98\u65b9）\u5b9a\u4f4d：HuggingFace\u751f\u6001\u7684\u6807\u51c6\u63a8\u7406\u670d\u52a1\u3002\u6838\u5fc3\u7279\u6027：- Rust\u5b9e\u73b0：\u4f4e\u5ef6\u8fdf\u3001\u9ad8\u5e76\u53d1- \u6a21\u578b\u517c\u5bb9：\u4efb\u4f55HuggingFace\u6a21\u578b- \u751f\u4ea7\u7279\u6027：Prometheus metrics\u3001OpenTelemetry- \u591a\u67b6\u6784：\u652f\u6301GPT-2\u3001LLaMA\u3001Mistral\u3001Mixtral\u3001Qwen- \u7b80\u5355\u90e8\u7f72：Docker\u955c\u50cf\u5b8c\u55842026\u5e74\u72b6\u6001：v3.0+，\u4f01\u4e1a\u751f\u4ea7\u73af\u5883\u4f7f\u7528\u7387\u63d0\u5347\u3002## \u4e8c\u3001\u6027\u80fd\u5bf9\u6bd4（\u57fa\u4e8e2026\u5e74Q2\u516c\u5f00benchmark）\u6d4b\u8bd5\u6761\u4ef6：Llama 4 70B，4\u00d7H100，128K\u4e0a\u4e0b\u6587，batch=8| \u670d\u52a1\u5668 | \u541e\u5410\u91cf（tokens\/s） | \u5ef6\u8fdfP99（ms） | \u663e\u5b58\u6548\u7387 ||——–|——————|————–|———|| vLLM | 8500 | 220 | 78% || SGLang | 9200 | 200 | 82% || TensorRT-LLM | 9800 | 180 | 85% || LMDeploy | 8800 | 210 | 80% || TGI | 7500 | 250 | 72% |\u6ce8\u610f：\u6027\u80fd\u6570\u636e\u53d7\u786c\u4ef6\u3001\u6a21\u578b\u3001batch\u914d\u7f6e\u5f71\u54cd，\u5b9e\u9645\u90e8\u7f72\u9700\u8981\u6839\u636e\u573a\u666fbenchmark\u3002## \u4e09\u3001\u7279\u6027\u5bf9\u6bd4\u77e9\u9635| \u7279\u6027 | vLLM | SGLang | TensorRT-LLM | LMDeploy | TGI ||——|——|——–|————–|———-|—–|| PagedAttention | \u2705 | \u2705 | \u2705 | \u2705 | \u274c || Prefix Caching | \u2705 | \u2705\u2705 | \u2705 | \u2705 | \u2705 || Speculative Decoding | \u2705 | \u2705 | \u2705 | \u2705 | \u274c || \u7ed3\u6784\u5316\u751f\u6210 | \u26a0\ufe0f | \u2705\u2705 | \u26a0\ufe0f | \u26a0\ufe0f | \u26a0\ufe0f || FP8\/INT4 | \u2705 | \u2705 | \u2705\u2705 | \u2705 | \u2705 || \u56fd\u4ea7GPU | \u26a0\ufe0f | \u26a0\ufe0f | \u274c | \u2705\u2705 | \u26a0\ufe0f || OpenAI\u517c\u5bb9 | \u2705 | \u2705 | \u26a0\ufe0f | \u2705 | \u2705 || \u591a\u6a21\u6001 | \u2705 | \u2705 | \u2705 | \u2705 | \u26a0\ufe0f || \u5206\u5e03\u5f0f | \u2705\u2705 | \u2705 | \u2705\u2705 | \u2705 | \u2705 || \u793e\u533a\u6d3b\u8dc3\u5ea6 | \u2705\u2705 | \u2705\u2705 | \u2705 | \u2705 | \u2705 |## \u56db\u3001\u56db\u5927\u4e1a\u52a1\u573a\u666f\u7684\u9009\u578b\u5efa\u8bae### 4.1 \u573a\u666f\u4e00：\u901a\u7528LLM\u670d\u52a1（OpenAI\u66ff\u4ee3）\u63a8\u8350：vLLM\u7406\u7531：- \u6027\u80fd\u7a33\u5b9a，\u793e\u533a\u6d3b\u8dc3，\u95ee\u9898\u54cd\u5e94\u5feb- OpenAI API\u517c\u5bb9，\u8fc1\u79fb\u6210\u672c\u4f4e- \u6587\u6863\u5b8c\u5584，\u90e8\u7f72\u7b80\u5355bash# vLLM\u90e8\u7f72\u793a\u4f8bpython -m vllm.entrypoints.openai.api_server \\\\ –model meta-llama\/Llama-4-70B \\\\ –tensor-parallel-size 4 \\\\ –gpu-memory-utilization 0.9 \\\\ –enable-prefix-cachingtext### 4.2 \u573a\u666f\u4e8c：Agent\u63a8\u7406 + \u7ed3\u6784\u5316\u8f93\u51fa\u63a8\u8350：SGLang\u7406\u7531：- RadixAttention\u5bf9\u591a\u8f6eAgent\u5bf9\u8bdd\u7279\u522b\u6709\u6548- \u7ed3\u6784\u5316\u751f\u6210（JSON Schema\u3001Grammar）\u662fAgent\u521a\u9700- \u7f16\u7a0b\u6a21\u578b\u53cb\u597dpythonimport sglang as sgl@sgl.functiondef tool_call(s, question: str): s += "You are a helpful assistant.\\\\n" s += "Use the following tools:\\\\n" s += "1. search_web(query)\\\\n" s += "2. query_database(sql)\\\\n" s += f"\\\\nQuestion: {question}\\\\n" s += sgl.gen("response", max_tokens=512)# \u591a\u8f6e\u590d\u7528\u524d\u7f00state = tool_call.run(…)state2 = tool_call.run(…, prefix_state=state) # \u590d\u7528prefix### 4.3 \u573a\u666f\u4e09：\u6781\u81f4\u6027\u80fd（H100\/H200\u96c6\u7fa4）\u63a8\u8350：TensorRT-LLM\u7406\u7531：- \u6027\u80fd\u6bd4vLLM\u9ad815-20%- \u6df1\u5ea6\u4f18\u5316Hopper\/Blackwell\u67b6\u6784- \u4e0eTriton Inference Server\u96c6\u6210python# TensorRT-LLM\u90e8\u7f72trtllm-build \\\\ –checkpoint_dir .\/llama4_70b \\\\ –output_dir .\/engine \\\\ –max_batch_size 32 \\\\ –max_input_len 32768 \\\\ –max_output_len 4096 \\\\ –gemm_plugin fp8 \\\\ –use_inflight_batchingtext### 4.4 \u573a\u666f\u56db：\u56fd\u4ea7GPU\/\u4fe1\u521b\u73af\u5883\u63a8\u8350：LMDeploy\u7406\u7531：- \u539f\u751f\u652f\u6301\u534e\u4e3a\u6607\u817e\u3001\u5bd2\u6b66\u7eaa\u3001\u6d77\u5149DCU- \u4e0e\u56fd\u4ea7\u6a21\u578b（InternLM\u3001Qwen\u3001DeepSeek）\u6df1\u5ea6\u9002\u914d- \u4fe1\u521b\u5408\u89c4bash# LMDeploy\u90e8\u7f72（\u6607\u817e）lmdeploy serve api_server \\\\ –model-path internlm\/internlm3-70b \\\\ –backend turbomind \\\\ –device ascend \\\\ –tp 8### 4.5 \u573a\u666f\u4e94：\u5feb\u901f\u96c6\u6210（PoC\/\u539f\u578b）\u63a8\u8350：TGI\u7406\u7531：- Docker\u955c\u50cf\u5f00\u7bb1\u5373\u7528- \u4efb\u4f55HuggingFace\u6a21\u578b\u76f4\u63a5\u90e8\u7f72- \u76d1\u63a7\u548c\u53ef\u89c2\u6d4b\u6027\u96c6\u6210bash# TGI\u90e8\u7f72docker run -d –gpus all \\\\ -p 8080:80 \\\\ -v ~\/.cache\/huggingface:\/data \\\\ ghcr.io\/huggingface\/text-generation-inference:latest \\\\ –model-id meta-llama\/Llama-4-70B \\\\ –num-shard 4text## \u4e94\u3001\u751f\u4ea7\u90e8\u7f72\u7684\u5173\u952e\u5de5\u7a0b\u5b9e\u8df5### 5.1 \u5b9e\u8df5\u4e00：\u8d1f\u8f7d\u6d4b\u8bd5\u5148\u884cpython# \u538b\u6d4b\u793a\u4f8bfrom locust import HttpUser, taskclass LLMUser(HttpUser): @task def generate(self): self.client.post("\/v1\/chat\/completions", json={ "model": "llama-4-70b", "messages": [{"role": "user", "content": "…"}], "max_tokens": 512 })\u538b\u6d4b\u5173\u952e\u6307\u6807：- \u541e\u5410\u91cf（tokens\/s）- \u5ef6\u8fdfP50\/P95\/P99- \u9519\u8bef\u7387- GPU\u5229\u7528\u7387- KV Cache\u547d\u4e2d\u7387### 5.2 \u5b9e\u8df5\u4e8c：\u76d1\u63a7\u544a\u8b66yaml# Prometheus\u544a\u8b66\u89c4\u5219groups:- name: llm_inference rules: – alert: HighLatency expr: histogram_quantile(0.99, llm_request_duration_seconds) > 5 for: 5m annotations: summary: "LLM\u63a8\u7406P99\u5ef6\u8fdf\u8d85\u8fc75\u79d2" – alert: LowGPULoading expr: avg(llm_gpu_utilization) < 0.6 for: 10m annotations: summary: "GPU\u5229\u7528\u7387\u957f\u671f\u4f4e\u4e8e60%，\u8003\u8651\u7f29\u5bb9" – alert: HighErrorRate expr: rate(llm_errors_total[5m]) > 0.05 for: 2m annotations: summary: "LLM\u63a8\u7406\u9519\u8bef\u7387\u8d85\u8fc75%"text### 5.3 \u5b9e\u8df5\u4e09：\u5f39\u6027\u6269\u7f29\u5bb9yaml# K8s HPA\u914d\u7f6eapiVersion: autoscaling\/v2kind: HorizontalPodAutoscalermetadata: name: vllm-hpaspec: scaleTargetRef: name: vllm minReplicas: 2 maxReplicas: 10 metrics: – type: Pods pods: metric: name: vllm_queue_size target: type: AverageValue averageValue: "5"### 5.4 \u5b9e\u8df5\u56db：\u6a21\u578b\u7248\u672c\u7ba1\u7406python# \u84dd\u7eff\u90e8\u7f72model_versions = { "v1": "meta-llama\/Llama-4-70B-v1", "v2": "meta-llama\/Llama-4-70B-v2" # \u65b0\u7248\u672c}# \u6d41\u91cf\u5207\u5206def route_request(prompt): if random.random() < 0.1: # 10%\u6d41\u91cf\u5230v2 return call_model("v2", prompt) return call_model("v1", prompt) # 90%\u6d41\u91cf\u5728v1text### 5.5 \u5b9e\u8df5\u4e94：\u6210\u672c\u4f18\u5316LLM\u63a8\u7406\u7684\u6210\u672c\u4f18\u5316\u624b\u6bb5（\u6309ROI\u6392\u5e8f）：1. \u91cf\u5316：FP16\u2192INT8（2\u500d\u6210\u672c\u4e0b\u964d，\u8d28\u91cf\u635f\u5931<1%）2. Speculative Decoding：2-3\u500d\u541e\u5410\u63d0\u53473. Prefix Caching：\u591a\u8f6e\u5bf9\u8bdd\u573a\u666f3-10\u500d4. Batch\u8c03\u4f18：\u627e\u5230\u6700\u4f73batch_size5. GPU\u5171\u4eab：MIG\/MPS，\u5355\u5361\u591a\u79df\u62376. Spot Instance：\u6210\u672c\u4e0b\u964d60-70%## \u516d\u30012026\u5e74\u4e0b\u534a\u5e74\u7684\u8d8b\u52bf1. vLLM 1.0：\u9884\u8ba12026\u5e74Q3\u53d1\u5e03v1.0，\u6027\u80fd\u518d\u63d0\u534730%\u30022. SGLang + Agent\u751f\u6001：\u6210\u4e3aAgent\u63a8\u7406\u7684\u4e8b\u5b9e\u6807\u51c6，\u4e0eLangGraph\u3001AutoGen\u6df1\u5ea6\u96c6\u6210\u30023. TensorRT-LLM + Blackwell：B200\/B300\u4e0a\u7684TensorRT-LLM\u6027\u80fd\u53ef\u80fd\u518d\u7ffb\u500d\u30024. \u56fd\u4ea7\u63a8\u7406\u670d\u52a1\u5668\u5d1b\u8d77：LMDeploy\u3001MindIE\u7b49\u56fd\u4ea7\u65b9\u6848\u5728\u4fe1\u521b\u5e02\u573a\u4efd\u989d\u6301\u7eed\u63d0\u5347\u30025. Serverless LLM：\u4e91\u5382\u5546\u63a8\u51fa"\u6309token\u8ba1\u8d39"\u7684Serverless LLM\u670d\u52a1（AWS Bedrock\u3001\u963f\u91cc\u4e91\u7075\u79ef\u3001\u706b\u5c71\u5f15\u64ce）\u3002## \u4e03\u3001\u5199\u5728\u6700\u540eLLM\u63a8\u7406\u670d\u52a1\u5668\u9009\u578b\u7684\u6838\u5fc3\u539f\u5219：\u6ca1\u6709"\u6700\u597d"，\u53ea\u6709"\u6700\u9002\u5408"\u3002- \u8ffd\u6c42\u6027\u80fd \u2192 TensorRT-LLM- \u8ffd\u6c42\u901a\u7528 \u2192 vLLM- \u8ffd\u6c42Agent \u2192 SGLang- \u8ffd\u6c42\u56fd\u4ea7\u5316 \u2192 LMDeploy- \u8ffd\u6c42\u7b80\u5355 \u2192 TGI\u5bf9\u5de5\u7a0b\u5e08\u6765\u8bf4，2026\u5e74\u4e0b\u534a\u5e74\u7684\u5efa\u8bae\u662f：1. \u4e0d\u8981\u5728\u9009\u578b\u4e0a\u82b1\u592a\u591a\u65f6\u95f4：\u4e94\u4e2a\u65b9\u6848\u90fd\u80fd\u7528，\u6027\u80fd\u5dee\u5f02\u4e0d\u8d85\u8fc730%2. \u5148\u628a\u57fa\u7840\u505a\u597d：\u76d1\u63a7\u3001\u6269\u7f29\u5bb9\u3001\u538b\u6d4b\u6bd4\u9009\u578b\u91cd\u898110\u500d3. \u5173\u6ce8\u6a21\u578b\u7684\u517c\u5bb9\u6027：\u65b0\u6a21\u578b（Llama 4\u3001Mistral 2\u3001Qwen3）\u901a\u5e38vLLM\/SGLang\u652f\u6301\u6700\u5feb4. \u5efa\u7acbbenchmark\u4f53\u7cfb：\u7528\u81ea\u5df1\u4e1a\u52a1\u7684\u771f\u5b9e\u6570\u636e\u538b\u6d4b，\u4e0d\u8981\u76f2\u4fe1\u516c\u5f00\u6570\u636e5. \u9884\u7559\u5347\u7ea7\u7a7a\u95f4：\u63a8\u7406\u670d\u52a1\u5668\u4ecd\u5728\u5feb\u901f\u6f14\u8fdb，\u67b6\u6784\u8981\u652f\u6301\u70ed\u5347\u7ea7\u8bb0\u4f4f：LLM\u63a8\u7406\u670d\u52a1\u5668\u7684\u7ade\u4e89\u662f"\u6027\u80fd\u00d7\u7a33\u5b9a\u6027\u00d7\u751f\u6001"\u7684\u7efc\u5408\u7ade\u4e89\u30022026\u5e74\u4e0b\u534a\u5e74，\u80dc\u8d1f\u624b\u4e0d\u5728"\u7edd\u5bf9\u6027\u80fd"，\u800c\u5728"\u5168\u94fe\u8def\u5de5\u7a0b\u80fd\u529b"\u3002<\/h3>\n","protected":false},"excerpt":{"rendered":"

2026\u5e746\u6708，LLM\u63a8\u7406\u670d\u52a1\u5668\u5df2\u7ecf\u4ece\\”\u767e\u82b1\u9f50\u653e\\”\u8d70\u5411\\”\u56db\u5f3a\u4e89\u9738\\”\u3002vLLM\u3001SGLang\u3001TensorRT-LLM\u3001LMDeploy\u3001Text Generation Inference (TGI) \u662f\u5f53\u524d\u6700\u4e3b\u6d41\u7684\u4e94\u4e2a\u9009\u62e9\u3002\u672c\u6587\u4ece\u6027\u80fd\u3001\u7279\u6027\u3001\u7a33\u5b9a\u6027\u3001\u751f\u6001\u56db\u4e2a\u7ef4\u5ea6\u505a\u7cfb\u7edf\u5bf9\u6bd4，\u5e76\u7ed9\u51fa\u57fa\u4e8e\u4e1a\u52a1\u573a\u666f\u7684\u9009\u578b\u5efa\u8bae\u3002
\n\u4e00\u3001\u4e94\u5927\u63a8\u7406\u670d\u52a1\u5668\u5168\u666f### 1.1 vLLM（UC Berkeley\u5f00\u6e90）\u5b9a\u4f4d：\u901a\u7528LLM\u63a8\u7406\u7684\u4e8b\u5b9e\u6807<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[6994,347,2681,50,43],"topic":[],"class_list":["post-92086","post","type-post","status-publish","format-standard","hentry","category-server","tag-sglang","tag-llama","tag-vllm","tag-50","tag-43"],"yoast_head":"\n2026\u5e74LLM\u63a8\u7406\u670d\u52a1\u5668\u751f\u4ea7\u7ea7\u5bf9\u6bd4\uff1avLLM\u3001SGLang\u3001TensorRT-LLM\u3001LMDeploy\u4e0eTGI\u7684\u5de5\u7a0b\u9009\u578b - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/92086.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"2026\u5e74LLM\u63a8\u7406\u670d\u52a1\u5668\u751f\u4ea7\u7ea7\u5bf9\u6bd4\uff1avLLM\u3001SGLang\u3001TensorRT-LLM\u3001LMDeploy\u4e0eTGI\u7684\u5de5\u7a0b\u9009\u578b - 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