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Question: You deployed an SGLang Serverless endpoint on RunPod successfully, while vLLM wasn’t working. Is that a good direction? Right?

Answer:

Yes. From what you posted, your SGLang Serverless deployment is working.

Evidence:

• Your request status is "COMPLETED".

• A worker processed the request:

  • "workerId": "grr2baqhs9xjvr"

• The model generated a valid OpenAI-compatible response:

  {
    "object": "chat.completion",
    "model": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
    "choices": [...]
  }
  

• The endpoint returned usage statistics (prompt_tokens, completion_tokens, etc.), meaning the inference stack completed end-to-end.

So your deployment pipeline is:

Client
    ↓
RunPod Serverless Endpoint
    ↓
SGLang Worker
    ↓
HF Model
    ↓
OpenAI-compatible JSON

That’s exactly what you want.

As for vLLM vs. SGLang, the landscape in mid-2026 looks roughly like this:

• vLLM

  • Excellent general-purpose inference engine
  • Very mature OpenAI-compatible server
  • Huge ecosystem
  • Strong continuous batching

• SGLang

  • Optimized for agentic workloads
  • Faster structured decoding
  • Better support for reasoning models and tool use
  • Increasing adoption by newer model releases

Many recent open-weight reasoning models (including several Tencent Hunyuan releases and other frontier open models) provide first-class SGLang support, so it’s not surprising that you found SGLang to be the smoother path for those models.

Since you’re building AI infrastructure and agents, learning both is valuable:

vLLM
    ↓
general inference
OpenAI API
batch serving

SGLang
    ↓
reasoning
tool calling
agents
structured generation

From your recent conversations, you’ve now gone through the whole deployment cycle:

• ✅ trained a GPT model from scratch
• ✅ experimented with vLLM
• ✅ deployed an SGLang Serverless endpoint on RunPod
• ✅ exposed an OpenAI-compatible API endpoint

That means you’re moving beyond model training into LLM serving infrastructure, which is another core piece of the stack.

The next natural step would be to benchmark your endpoint:

# latency
time curl ...

# throughput
hey -n 1000 -c 20 ...

# or
wrk

# streaming latency
curl -N ...

Measure metrics like:

• Time to First Token (TTFT)
• tokens/sec
• concurrent request throughput
• cold-start time
• cost per 1M output tokens

Those numbers become much more informative than simply verifying that the endpoint responds.

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