OPEN LINK ↗
// 53d agoTUTORIAL
Gemma 4 26B-A4B Fits 16 GB VRAM
This Reddit post argues that Gemma 4 26B A4B, especially the Unsloth IQ4_XS GGUF quant, is the strongest option for running Gemma 4 on a 16 GB GPU if you want to keep multimodal vision. The author claims that low-temperature sampling, conservative top-k/top-p settings, and a minimum image token budget materially improve coding and vision quality, while FP16 mmproj and a large fp16 KV cache still fit within the memory budget.
// ANALYSIS
Hot take: for users who care about local multimodal performance on constrained hardware, this reads less like a benchmark flex and more like a practical deployment recipe.
- –The post is a configuration guide first and a benchmark comparison second, so `tutorial` fits better than a pure benchmark category.
- –The core recommendation is the `unsloth/gemma-4-26B-A4B-it-GGUF` IQ4_XS quant, with `mmproj-F16.gguf` and tuned decoding parameters.
- –The main claim is that this setup balances quality, speed, and VRAM usage better than other quantizations the author tested, including Bartowski variants.
- –The vision advice is specific and actionable: keep `--image-min-tokens 300` and avoid wasting memory on higher-precision mmproj or KV quantization if it hurts quality.
- –The comparison against Qwen 3.5 27B is useful context, but it is still anecdotal and should be treated as a single-user field report rather than a controlled benchmark.
// TAGS
gemma-4-26b-a4bunslothggufllama-cppmoemultimodalvisionquantizationlocal-llm16gb-vramcoding
DISCOVERED
53d ago
2026-04-05
PUBLISHED
53d ago
2026-04-05
RELEVANCE
9/ 10
AUTHOR
Sadman782