[fix][5875912] Fix autoquant-autodeploy example (#878)

## What does this PR do?

**Type of change:** Bug fix <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->

**Overview:** ?
Please check Bug ticket

## Usage
<!-- You can potentially add a usage example below. -->

```python
# Add a code snippet demonstrating how to use this
```

## Testing
Tested with
```
./scripts/run_auto_quant_and_deploy.sh     --hf_ckpt ./models/Qwen/Qwen3-8B     --save_quantized_ckpt ./qwen3_8B_autoquant     --quant fp8     --effective_bits 10.0
```

## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes/No <!--- If No, explain
why. -->
- **Did you write any new necessary tests?**: Yes/No
- **Did you add or update any necessary documentation?**: Yes/No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes/No <!--- Only for new features, API changes, critical bug fixes or
bw breaking changes. -->

## Additional Information
<!-- E.g. related issue. -->


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

* **Refactor**
* Simplified LLM initialization by removing intermediate configuration
layer
  * Updated attention backend from triton to flashinfer

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Author: Fridah-nv

examples/llm_autodeploy/api_server.py

@@ -20,8 +20,7 @@
 
 import uvicorn
 from fastapi import FastAPI, HTTPException
-from tensorrt_llm._torch.auto_deploy import LLM, AutoDeployConfig
-from tensorrt_llm.builder import BuildConfig
+from tensorrt_llm._torch.auto_deploy import LLM
 from tensorrt_llm.llmapi.llm import RequestOutput
 from tensorrt_llm.sampling_params import SamplingParams
 from tensorrt_llm.serve.openai_protocol import (
@@ -45,11 +44,8 @@ def build_runner_from_config(args) -> LLM:
     """Builds a model runner from our config."""
     mto.enable_huggingface_checkpointing()
     model_kwargs = {"max_position_embeddings": args.max_seq_len, "use_cache": False}
-    build_config = BuildConfig(max_seq_len=args.max_seq_len, max_batch_size=args.max_batch_size)
-    build_config.plugin_config.tokens_per_block = args.max_seq_len
 
-    # setup AD config
-    ad_config = AutoDeployConfig(
+    llm = LLM(
         model=args.ckpt_path,
         compile_backend=args.compile_backend,
         device=args.device,
@@ -58,9 +54,8 @@ def build_runner_from_config(args) -> LLM:
         max_seq_len=args.max_seq_len,
         max_num_tokens=args.max_num_tokens,
         model_kwargs=model_kwargs,
-        attn_backend="triton",
+        attn_backend="flashinfer",
     )
-    llm = LLM(**ad_config.to_llm_kwargs())
 
     return llm