A user demonstrates running the NVFP4-quantized Qwen3.6-35B-A3B model on an RTX Pro 6000 Blackwell GPU, achieving approximately 2000 tokens per second in aggregate throughput while handling 30 concurrent image captioning streams. The configuration utilizes vLLM with the FLASHINFER attention backend and prefix caching to manage high concurrency. The Mixture of Experts (MoE) architecture activates only about 53-61% of experts even at high concurrency levels, allowing it to outperform dense models despite its larger parameter count. This setup proves that NVFP4 quantization on Blackwell hardware can efficiently handle multimodal workloads with significant parallelism without exhausting VRAM.
NVFP4 Qwen3.6-35B-A3B on Blackwell achieves ~2000 tps with 30 concurrent streams
Unified GGUF and llama.cpp fork enable audio and video for Nemotron-3-Nano-Omni
The author addresses silent failures in popular GGUF versions of Nemotron-3-Nano-Omni-30B, where audio and video inputs were ignored due to incomplete projector files and missing inference graphs. To fix this, a unified mmproj file containing the vision tower, full Parakeet/FastConformer audio encoder, and temporal video embedder has been released alongside a specialized llama.cpp fork.
Zero-shot Nemotron 3.5 Lightning Omni adds vision and audio via geometry match
The author has created Nemotron 3.5 Lightning-Omni by attaching pre-trained projectors from NVIDIA’s Nemotron-3-Nano-Omni to the text-only Nemotron 3.5 Lightning model, enabling image and audio understanding without any additional training.
NVIDIA serves Qwen 3.8 2.4T on GB300 NVL72 at 4K tokens/s per GPU
NVIDIA has demonstrated serving the Qwen 3.8 2.4T parameter model with configurable reasoning capabilities on its GB300 NVL72 hardware platform.
Microsoft releases Mage-VL, a codec-native streaming multimodal model
Microsoft has released Mage-VL, an efficient 4B-parameter multimodal foundation model for image and video understanding that uses a codec-native approach to streamline visual processing. The system separates video streams into anchor (I) frames and predicted (P) frames, retaining only patches where the codec allocates bits to reduce visual token consumption by over 75%.
Untuned Qwen3.6-27B outperforms tuned Nemotron Puzzle-75B in agentic tasks
An evaluation of agentic capabilities shows that the untuned Qwen3.6-27B model successfully completed all tested tasks using 6-9 tool calls, whereas the tuned Nemotron Puzzle-75B required hand-tuned prompts and significantly more turns to pass.