vLLM MBLT

vllm-mblt is an out-of-tree vLLM plugin that integrates Mobilint NPU runtime support into the vLLM serving and benchmarking stack.

It provides a custom vLLM platform, worker, and model registry hooks so Mobilint-optimized LLM/VLM artifacts can be served through familiar vLLM commands and OpenAI-compatible APIs.

Highlights

• Out-of-tree vLLM plugin: registers the mblt platform without patching vLLM itself.
• Mobilint NPU worker: dispatches text-generation and multimodal execution to Mobilint runtime models.
• Model registry integration: supports Mobilint wrappers for Llama, HyperCLOVAX, EXAONE/EXAONE4, Qwen2/3, and Qwen2/3-VL families.
• Runtime-aware scheduling: reads model-configured npu_prefill_chunk_size and max_batch_size values to tune chunked prefill and scheduler concurrency automatically.
• vLLM benchmark compatibility: works with vllm serve, vllm bench serve, and vllm bench throughput.

Requirements

• Python 3.10+
• vllm==0.11.2
• mblt-model-zoo[transformers] >= 2.1.0
• A Mobilint NPU environment. If you are not yet a Mobilint customer, please contact tech-support@mobilint.com.

The package pins vLLM for compatibility:

vllm>=0.11.2,<=0.11.2

Installation

Install from PyPI:

pip install vllm-mblt

Or install the latest source checkout:

git clone https://github.com/mobilint/vllm-mblt.git
cd vllm-mblt
python -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install -e .

Quick Start

1. Verify Plugin Registration

After installation, run:

vllm --help

You should see plugin logs indicating that the Mobilint mblt platform plugin has been discovered and activated.

2. Serve a Text Model

vllm serve mobilint/Llama-3.2-1B-Instruct --trust-remote-code

Then query the OpenAI-compatible endpoint:

curl http://127.0.0.1:8000/v1/models

3. Serve a VLM Model

Qwen2-VL and Qwen3-VL Mobilint models can be loaded through the same vLLM server path:

vllm serve mobilint/Qwen2-VL-2B-Instruct --trust-remote-code

vllm serve mobilint/Qwen3-VL-2B-Instruct --trust-remote-code

Current Mobilint Qwen2/3-VL notes:

• The worker loads VLMs through AutoModelForImageTextToText.
• Image inputs are processed through vLLM's multimodal pipeline and merged into Mobilint language-model prompt embeddings inside the custom worker.
• The NPU path currently supports exactly one image in the initial multimodal request.
• Subsequent turns in the same session must be text-only or reuse the same image-token position.
• Video inputs are not supported by the current Mobilint Qwen2/3-VL NPU path.

Runtime Tuning

Runtime Layout Overrides

By default, vllm-mblt follows the runtime layout encoded in the Mobilint model artifact/config. Use --model-loader-extra-config only when you intentionally want to override runtime placement or testing knobs.

Runtime settings such as dev_no, target_cores, target_clusters, core_mode, and max_batch_size can be provided through --model-loader-extra-config. For detailed core_mode and multicore runtime layout guidance, see the Mobilint multicore documentation.

vllm serve mobilint/Llama-3.2-1B-Instruct \
  --trust-remote-code \
  --model-loader-extra-config '{"dev_no": 0, "target_cores": ["1:0"]}'

For VLMs such as mobilint/Qwen3-VL-2B-Instruct, shared runtime layout keys are applied to both Mobilint submodules by forwarding them as model-zoo VLM subconfig keys (vision_* and text_*). Use explicit prefixed keys when the vision encoder and text model need different placement:

vllm serve mobilint/Qwen3-VL-2B-Instruct \
  --trust-remote-code \
  --model-loader-extra-config '{"dev_no": 0, "core_mode": "global4", "vision_core_mode": "single"}'

For VLM-specific MXQ path overrides, use vision_mxq_path and/or text_mxq_path; a single top-level mxq_path is only meaningful for single-module text models.

Chunked Prefill Auto-Tuning

If a model config includes npu_prefill_chunk_size, vllm-mblt uses it to tune vLLM chunked prefill.

• Integer values are used directly.
• Dict values are selected by core_mode.
• core_mode is resolved from --model-loader-extra-config first, then from the model config default.
• The selected value is applied to vLLM's max_num_batched_tokens for chunked prefill.
• If no matching value is found, vllm-mblt falls back to 128.
• For batch-compiled models with max_batch_size > 1, the effective chunked prefill limit is clamped to 128 to match the qbruntime batch execution limit used by the worker.

Example model config:

{
  "npu_prefill_chunk_size": {
    "single": 64,
    "global4": 256,
    "global8": 512
  }
}

With this command, vllm-mblt selects 256 for global4:

vllm serve mobilint/YourModel \
  --trust-remote-code \
  --model-loader-extra-config '{"dev_no": 0, "core_mode": "global4", "target_clusters": [0]}'

If you also pass --max-num-batched-tokens, the effective value becomes the smaller of the user-provided value and the model-configured npu_prefill_chunk_size.

Use --block-size only when you intentionally want to override the model-configured/default block size:

vllm serve mobilint/Llama-3.2-1B-Instruct \
  --trust-remote-code \
  --block-size 64

Model-Configured Batch Capacity

If a model config includes max_batch_size, vllm-mblt uses that value to support batch-compiled Mobilint models.

• The worker uses max_batch_size for KV cache memory sizing.
• The platform applies it to vLLM max_num_seqs automatically.
• You do not need to pass --max-num-seqs unless you intentionally want a smaller scheduler cap.
• max_batch_size also supports the same core_mode keyed dict form as npu_prefill_chunk_size.
• For local testing, --model-loader-extra-config '{"max_batch_size": 32}' overrides the model config value.

Example:

vllm serve mobilint/Llama-3.2-1B-Instruct-Batch32 --trust-remote-code

For batch-compiled MXQs such as mobilint/Llama-3.2-1B-Instruct-Batch32, the plugin also caps the effective chunked prefill limit to 128, even when the model config advertises a larger npu_prefill_chunk_size.

Benchmarking

This repository includes sonnet.txt, which can be used with vLLM benchmark commands.

Serve Benchmark

Terminal 1:

vllm serve --model mobilint/Llama-3.2-1B-Instruct --trust-remote-code

Terminal 2:

vllm bench serve --model mobilint/Llama-3.2-1B-Instruct \
  --trust-remote-code \
  --port 8000 \
  --num-warmups 1 \
  --dataset-name sonnet \
  --dataset-path sonnet.txt \
  --num-prompts 10

Throughput Benchmark

vllm bench throughput --model mobilint/Llama-3.2-1B-Instruct \
  --trust-remote-code \
  --dataset-name sonnet \
  --dataset-path sonnet.txt \
  --num-prompts 10

Notes:

• vllm bench serve uses a separate server process; vllm bench throughput runs the engine directly.
• vllm bench serve --max-concurrency is a benchmark client load setting, not the server-side scheduler limit.
• Reported latency and throughput are environment-dependent. Capture results from your target board for documentation or performance comparisons.

Supported Model Families

vllm-mblt registers Mobilint model wrappers for:

Model artifacts are available through Mobilint model repositories such as the Mobilint Hugging Face Hub.

Cache Behavior

MbltWorker uses snapshot-based KV cache reuse with these policies:

• Event-driven dump, not every step.
• Reuse live cache for same-request continuous decode.
• Keep finished-session snapshots for prefix reuse.
• Evict finished snapshots with an LRU cap of 16 sessions.

Implementation file: vllm_mblt/mblt_worker.py

Tests

python -m pytest tests

Project Structure

vllm_mblt/
├── __init__.py                 # vLLM plugin and model registration entry points
├── mblt_platform.py            # platform config overrides and runtime-aware defaults
├── mblt_worker.py              # custom worker, prefill/decode flow, KV snapshot logic
└── models/                     # Mobilint model wrappers for LLM/VLM families

tests/
├── test_kv_cache_swap_spec.py
├── test_mblt_platform_prefill.py
└── test_mblt_worker_optimizations.py