MOVZXandHinata
43fbd6510b
Adds an opt-in backend, --engram-config '{"host_file_gather": true}', that
serves Qwen4Exp PLE rows from the checkpoint files through the page cache
instead of allocating the 47.7 GiB table on GPU or in pinned host memory.
- Qwen4ExpPLEFileGatherEmbedding allocates no table rows; load_weights keeps
the default loader's zero-copy safetensors views of each shard. At bind
(post-load hook) every view must be file-backed per /proc/self/maps (HF
cache symlinks work; heap/pinned/anonymous memory is rejected) and the
shards must cover the whole table.
- Each step, Qwen4ExpModelState.prepare_inputs computes the PLE n-gram ids,
copies them to pinned host memory, syncs the stream once, issues
POSIX_FADV_WILLNEED over the coalesced page ranges of the step's rows,
gathers the rows on the host, and copies them into a fixed device staging
buffer. The forward reads only the staging buffer, so FULL CUDA graph
capture is unaffected.
- Rows stay in the checkpoint dtype (FP8 table stays FP8, dequantized on the
GPU); dummy loads serve zeros; weight reload is rejected; takes precedence
over cpu_offload; rejected on ROCm and with elastic EP.
Co-Authored-By: Hinata <hinata@idihore.id>
Signed-off-by: MOVZX <movzx@yahoo.com>
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Welcome to vLLM
Easy, fast, and cheap LLM serving for everyone
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vLLM is a fast and easy-to-use library for LLM inference and serving.
Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has grown into one of the most active open-source AI projects built and maintained by a diverse community of many dozens of academic institutions and companies from over 2000 contributors.
Where to get started with vLLM depends on the type of user. If you are looking to:
- Run open-source models on vLLM, we recommend starting with the Quickstart Guide
- Build applications with vLLM, we recommend starting with the User Guide
- Build vLLM, we recommend starting with Developer Guide
For information about the development of vLLM, see:
vLLM is fast with:
- State-of-the-art serving throughput
- Efficient management of attention key and value memory with PagedAttention
- Continuous batching of incoming requests, chunked prefill, prefix caching
- Fast and flexible model execution with piecewise and full CUDA/HIP graphs
- Quantization: FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, GPTQ/AWQ, GGUF, compressed-tensors, ModelOpt, TorchAO, and more
- Optimized attention kernels including FlashAttention, FlashInfer, TRTLLM-GEN, FlashMLA, and Triton
- Optimized GEMM/MoE kernels for various precisions using CUTLASS, TRTLLM-GEN, CuTeDSL
- Speculative decoding including n-gram, suffix, EAGLE, DFlash
- Automatic kernel generation and graph-level transformations using torch.compile
- Disaggregated prefill, decode, and encode
vLLM is flexible and easy to use with:
- Seamless integration with popular Hugging Face models
- High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
- Tensor, pipeline, data, expert, and context parallelism for distributed inference
- Streaming outputs
- Generation of structured outputs using xgrammar or guidance
- Tool calling and reasoning parsers
- OpenAI-compatible API server, plus Anthropic Messages API and gRPC support
- Efficient multi-LoRA support for dense and MoE layers
- Support for NVIDIA GPUs, AMD GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more.
vLLM seamlessly supports 200+ model architectures on HuggingFace, including:
- Decoder-only LLMs (e.g., Llama, Qwen, Gemma)
- Mixture-of-Expert LLMs (e.g., Mixtral, DeepSeek-V3, Qwen-MoE, GPT-OSS)
- Hybrid attention and state-space models (e.g., Mamba, Qwen3.5)
- Multi-modal models (e.g., LLaVA, Qwen-VL, Pixtral)
- Embedding and retrieval models (e.g., E5-Mistral, GTE, ColBERT)
- Reward and classification models (e.g., Qwen-Math)
Find the full list of supported models here.
For more information, check out the following:
- vLLM announcing blog post (intro to PagedAttention)
- vLLM paper (SOSP 2023)
- How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al.
- vLLM Meetups