Gemma 4 31B beats several frontier models on the FoodTruck Bench
Gemma 4 31B takes an incredible 3rd place on FoodTruck Bench, beating GLM 5, Qwen 3.5 397B and all Claude Sonnets! I'm looking forward to how they'll explain the result. Based on the previous models that failed to finish the run, it would seem that Gemma 4 handles long horizon tasks better and actually listens to its own advice when planning for the next day of the run. submitted by /u/Nindaleth [link] [comments]
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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 $(getent group render | cut -d: -f3) --ipc=host aiinfos/vllm-gfx906-mobydick:latest FLASH_ATTENTION_TRITON_AMD_ENABLE="TRUE" OMP_NUM_THREADS=4 VLLM_LOGGING_LEVEL=DEBUG vllm serve \ /models/gemma-4-31B-it-AWQ-4bit \ --served-model-name gemma-4-31B-it-AWQ-4bit \ --dtype float16 \ --max-model-len auto \ --gpu-memory-utilization 0.95 \ --enable-auto-tool-choice \ --tool-call-parser gemma4 \ --reasoning-parser gemma4 \ --mm-processor-cache-gb 1
Vllm gemma4 26b a4b it-nvfp4 run success
#!/usr/bin/env bash set -euo pipefail BASE_DIR="/mnt/d/AI/docker-gemma4" PATCH_DIR="$BASE_DIR/nvfp4_patch" BUILD_DIR="$BASE_DIR/build" HF_CACHE_DIR="$BASE_DIR/hf-cache" LOG_DIR="$BASE_DIR/logs" PATCH_FILE="$PATCH_DIR/gemma4_patched.py" DOCKERFILE_PATH="$BUILD_DIR/Dockerfile" BASE_IMAGE="vllm/vllm-openai:gemma4" PATCHED_IMAGE="vllm-gemma4-nvfp4-patched" CONTAINER_NAME="vllm-gemma4-nvfp4" MODEL_ID="bg-digitalservices/Gemma-4-26B-A4B-it-NVFP4" SERVED_MODEL_NAME="gemma-4-26b-a4b-it-nvfp4" GPU_MEMORY_UTILIZATION="0.88" MAX_MODEL_LEN="512" MAX_NUM_SEQS="1" PORT=" " PATCH_URL=" https://huggingface.co/bg-digitalservices/Gemma-4-26B-A4B-it-NVFP4/resolve/main/gemma4_patched.py?download=true " if [[ -z "${HF_TOKEN:-}" ]]; then echo "[ERROR] HF_TOKEN environment variable is empty." echo "Please run th
Prompts you use to test/trip up your LLMs
I'm obsessed with finding prompts to test the quality of different local models. I've pretty much landed on several that I use across the board. Tell me about the Apple A6 (a pass is if it mentions Apple made their own microarchitecture called swift for the CPU cores, the main thing that the A6 is historically known for as the first Apple SOC to do it. This tests if it is smart enough to mention historically relevant information first) Tell me about the history of Phoenix's freeway network (A pass is if it gives a historical narration instead of just listing freeways. We asked for history, after all. Again, testing for its understanding of putting relevant information first.) Tell me about the Pentium D. Why was it a bad processor ( A pass is it it mentions that it glued two separate penti
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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]
Prompts you use to test/trip up your LLMs
I'm obsessed with finding prompts to test the quality of different local models. I've pretty much landed on several that I use across the board. Tell me about the Apple A6 (a pass is if it mentions Apple made their own microarchitecture called swift for the CPU cores, the main thing that the A6 is historically known for as the first Apple SOC to do it. This tests if it is smart enough to mention historically relevant information first) Tell me about the history of Phoenix's freeway network (A pass is if it gives a historical narration instead of just listing freeways. We asked for history, after all. Again, testing for its understanding of putting relevant information first.) Tell me about the Pentium D. Why was it a bad processor ( A pass is it it mentions that it glued two separate penti
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