9 posts
Day-0 vLLM support for Qwen3.8-2.4T-A95B: a 2.4-trillion-parameter hybrid MoE model served out of the box, with FP8/BF16 checkpoints plus NVFP4 and MXFP4 quantized weights, and co-developed kernels on NVIDIA and AMD hardware.
How AutoRound integrates with vLLM-Omni to serve W4A16 quantized multimodal, diffusion, image, and video models with smaller checkpoints, preserved quality, Intel XPU acceleration, and NVIDIA GPU support.
How Laguna XS.2 is served and optimized in vLLM using first-class model integration, a DFlash speculator trained with Speculators, and FP8, NVFP4, INT4, and INT8 checkpoints from LLM Compressor.
A vLLM study comparing TurboQuant KV-cache quantization with BF16 and FP8 across long-context and reasoning workloads, showing where 4-bit variants help, where accuracy drops, and why FP8 remains the default choice.
What vLLM FP8 KV-cache validation found across Hopper and Blackwell, covering attention quantization, Flash Attention 3 fixes, memory savings, decode speedups, and layers to skip.
What DeepSeek-V3.2 and DeepSeek-R1 benchmark results show on NVIDIA GB300 with vLLM, covering NVFP4 quantization, TP and EP deployment, throughput, and reproducible setup details.
How Intel AutoRound integrates with LLM Compressor to produce low-bit quantized checkpoints for vLLM, using tuning-based PTQ, W4A16 and related formats, compressed-tensors compatibility, and lightweight calibration.
How PTPC-FP8 quantization improves vLLM performance on AMD ROCm by combining per-token activation scaling and per-channel weight scaling for near-BF16 accuracy with FP8 speed.
How vLLM supports Meta Llama 3.1 models, including 128K context, Llama 3.1 405B serving, chunked prefill, FP8 quantization, tensor and pipeline parallelism, CPU offloading, and early performance results.
