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LOADING DATASHEET
by Google
RANKED #225 OF 232 ASSISTANTS · OVERALL #342 OF 6,566 · VIBE SCORE 5.0 · 36 VOICES
Google's Gemma 4 12B Instruct is an open-weight multimodal model for text, image, audio, and video understanding, with tool calling and structured output support.
0 mentions
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26 weeks · 43 voices
HOLDING UP
Recent vibe is steady. No clear fade in the crowd.
Open weights. You can use a hosted service, or download it and run it yourself, free.
Open weights: download vllm translategemma 12B it once and it's yours. No subscription, no rate limits, works offline.
12B parameters, a Q4 file is about 7.5 GB to 8 GB
NVIDIA GeForce RTX 3060 12GB
12 GB VRAM
~$280 NEWQ4_K_M quantization with 2k context
NVIDIA GeForce RTX 4070 Ti Super 16GB
16 GB VRAM
~$780 NEWQ8_0 or FP8 quantization with full context
NVIDIA GeForce RTX 4090 24GB
24 GB VRAM
~$1750 NEWFull precision BF16 or unquantized vLLM batching
The three cards above are researched picks. Search the machine you actually have — we only say yes if it should reply at a usable speed, not just load the weights and crawl.
HARDWARE PICKS & PRICES ARE RESEARCHED FROM THE LIVE WEB AND REFRESHED AUTOMATICALLY. TREAT THEM AS BALLPARK, NOT GOSPEL.
Everyday jobs on each of those rigs: how long vllm translategemma 12B it takes, and how good the crowd says it is at that kind of work.
| THE JOB | BARE MINIMUMNVIDIA GeForce RTX 3060 12GB | SWEET SPOTNVIDIA GeForce RTX 4070 Ti Super 16GB | FULL POWERNVIDIA GeForce RTX 4090 24GB |
|---|---|---|---|
| Write an emaila paragraph or two, drafted from a one-line brief | ~14 S | ~8 S | ~5 S |
| Summarize a documenta long report boiled down to the points that matter | ~43 S★★★★★ | ~23 S★★★★★ | ~14 S★★★★★ |
| Build a websitea small landing page, markup and styles together | ~3.8 MIN★★★★★ | ~2.1 MIN★★★★★ | ~73 S★★★★★ |
| Build a backendan API with routes, storage and tests | ~12 MIN★★★★★ | ~6.4 MIN★★★★★ | ~3.8 MIN★★★★★ |
| Build a gamea playable browser game in one file | ~7.1 MIN★★★★★ | ~3.8 MIN★★★★★ | ~2.3 MIN★★★★★ |
STARS ARE THE COMMUNITY SCORE FOR THAT KIND OF WORK, DOCKED FOR HOW SQUASHED THE WEIGHTS GET AT EACH TIER. TIMES ARE COMPUTED FROM RESEARCHED SPEEDS. BALLPARK, NOT GOSPEL.
People are mostly frustrated with vllm translategemma 12B it right now.
33 POSTS · 3 COMMENTS · GITHUB · REDDIT
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Other models the crowd has fully reviewed, starting with text models like this one.
The best model is the one you can actually run
Distilled DeepSeek into Gemma 4 26B-A4B vs 12B. Not very useful, but I learned a lot.
Gemma 4 12B Q3: +8.55% Coding Performance From Tensor-Level Quantization Allocation
Gemma 4 Quadruple Release, 12B, 12B QAT, 26B-A4B QAT and 31B QAT Uncensored Heretics!
[Study/Models] Flint: Compressing Reasoning Without Breaking It
Gemma4-12B-QAT Uncensored Balanced is out with MTP (~60% speed boost)!
Wuli-art/Gemma-4-for-Qwen-Image-Edit-2511-Prompt-Extend · Hugging Face
Gemma4-12B-QAT Uncensored Balanced is out with MTP (~60% speed boost)!
Infected weights
Benchmarks iGPU integrated Radeon 680M models
Gemma 4 12B on Ollama: the GGUF is multimodal (CLIP crashes 0.30.2), it's a reasoning model, and Q4 has token glitches — fixes + Q4-vs-Q8 numbers
TensorSharp supports Vulkan backend