Model Releases
Running Gemma 4 QAT 12B on an 8GB GPU at 16k context — measured the KV-cache tradeoffs
This post discusses running Google's Gemma 4 QAT (Quantized Aware Training) 12B model on a GPU with 8GB of memory while maintaining a 16k token context window. The author likely shares performance ben
This post discusses running Google's Gemma 4 QAT (Quantized Aware Training) 12B model on a GPU with 8GB of memory while maintaining a 16k token context window. The author likely shares performance benchmarks, memory optimization techniques, and tradeoffs involved in running a quantized large language model with extended context on resource-constrained hardware. The discussion probably covers practical insights about KV-cache (key-value cache) management and how quantization impacts model quality and inference speed on limited VRAM.
Source: r/ollama | 2026-06-11