Exclusive | Pentagon Used Anthropic’s Claude in Maduro Venezuela Raid - WSJ
Exclusive | Pentagon Used Anthropic’s Claude in Maduro Venezuela Raid WSJ
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Bench 2xMI50 Qwen3.5 27b vs Gemma4 31B (vllm-gfx906-mobydick)
Inference engine used (vllm fork) : https://github.com/ai-infos/vllm-gfx906-mobydick/tree/main Huggingface Quants used: QuantTrio/Qwen3.5-27B-AWQ vs cyankiwi/gemma-4-31B-it-AWQ-4bit Relevant commands to run : docker run -it --name vllm-gfx906-mobydick -v ~/llm/models:/models --network host --device=/dev/kfd --device=/dev/dri --group-add video --group-add $(getent group render | cut -d: -f3) --ipc=host aiinfos/vllm-gfx906-mobydick:latest FLASH_ATTENTION_TRITON_AMD_ENABLE="TRUE" OMP_NUM_THREADS=4 VLLM_LOGGING_LEVEL=DEBUG vllm serve \ /models/gemma-4-31B-it-AWQ-4bit \ --served-model-name gemma-4-31B-it-AWQ-4bit \ --dtype float16 \ --max-model-len auto \ --gpu-memory-utilization 0.95 \ --enable-auto-tool-choice \ --tool-call-parser gemma4 \ --reasoning-parser gemma4 \ --mm-processor-cache-gb 1
RTX 5090 gemma4-26b TG performance report
Nothing exhaustive... but I thought I'd report what I've seen from early testing. I'm running a modified version of vLLM that has NVFP4 support for gemma4-26b. Weights come in around 15.76 GiB and the remainder is KV cache. I'm running full context as well. For a "story telling" prompt and raw output with no thinking, I'm seeing about 150 t/s on TG. TTFT in streaming mode is about 80ms. Quality is good! submitted by /u/Nice_Cellist_7595 [link] [comments]
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