Gemma 4 12B: 256K Context, 8GB GPU Multimodal (2026)
Gemma 4 12B is Google's June 2026 open-weight model with 256K context, native audio/video input, and Apache 2.0 licensing on just 8GB of VRAM.
Gemma 4 12B is Google DeepMind's mid-sized open-weight model (released June 3, 2026) with a 262,144-token context window, native audio and video input, and a reported 78.8% GPQA Diamond score. Its encoder-free design lets it run full multimodal inference on an 8GB GPU, licensed under Apache 2.0 for unrestricted commercial use.
Gemma 4 12B, released June 3, 2026 by Google DeepMind, is an open-weight multimodal model with a 262,144-token context window and reported 78.8% on GPQA Diamond. Its encoder-free architecture reads text, images, audio and video directly, and it runs on an 8GB consumer GPU under an Apache 2.0 license.
Provider: Google DeepMind · Family: Gemma 4
Context window: 262,144 tokens
Input modalities: text, image, audio, video · Output: text, tool-calls
About Gemma 4 12B
Gemma 4 12B is an open-weight multimodal language model built by Google DeepMind, released June 3, 2026 as the mid-sized addition to the Gemma 4 family that launched April 2, 2026 alongside the E2B, E4B, 26B-A4B mixture-of-experts, and 31B dense variants. It sits between the tiny edge-focused E4B and the larger 26B/31B models, aimed at developers who need a single laptop-runnable model that reads text, images, audio, and video without shipping four separate pipelines. It uses a dense, decoder-only Transformer built on the same backbone as Gemma 4 31B Dense, with roughly 12 billion parameters. The defining architectural choice is going encoder-free. Earlier multimodal stacks bolt on a heavyweight vision encoder (often 500M+ parameters) and a separate audio encoder, then fuse their embeddings into the language model. Gemma 4 12B drops both: a 35M-parameter linear vision embedder projects raw 48x48 image patches directly into the model's hidden dimension in a single matrix multiplication, with spatial position added through factorized X/Y coordinate lookup tables instead of a full attention stack. Audio gets the same lightweight-projection treatment. Google reports this cuts multimodal inference latency meaningfully versus the encoder-heavy designs used in Gemma 3 and most competing open models, since there is no separate encoder forward pass before the language model even starts. On benchmarks, independent write-ups (Techsy, BuildFastWithAI, AICybr) report Gemma 4 12B scoring roughly 78.8% on GPQA Diamond and 94.9% on DocVQA, positioning it well ahead of Gemma 3's comparable-size tier on graduate-level reasoning and document understanding. Google has not published an official SWE-bench or MMLU-Pro score for the 12B variant specifically (those numbers exist for the larger 31B sibling, which posts 89.2% on AIME 2026 and 80.0% on LiveCodeBench v6), so treat the 12B reasoning figures as third-party reported rather than vendor-confirmed until Google ships a dedicated model card update. Context window is 262,144 tokens (256K), matching the rest of the Gemma 4 family, which lets a 12B-class model handle full codebases, long PDFs, or hour-plus video transcripts without chunking. Google has not published a separate max-output-token ceiling distinct from the shared context budget. Modality support is the headline feature: text, image, video (processed as sampled frame sequences), and audio input, with the E2B, E4B, and 12B checkpoints all sharing audio support (the larger 26B and 31B variants do not, per Google's own comparison). Capabilities include object detection, document and PDF parsing, screen and UI understanding, chart reading, multilingual OCR, handwriting recognition, and native function calling for agentic tool-use loops. Interleaved multimodal prompts (mixing text and images in any order) are supported natively rather than through a special API mode. Pricing is where Gemma 4 12B differs sharply from proprietary frontier models: it is free to download and self-host. At Q4_K_M quantization it needs about 6.6GB of VRAM, fitting comfortably on an 8GB consumer GPU or a 16GB unified-memory MacBook. Google shipped official quantization-aware training (QAT) checkpoints in both unquantized Q4_0 and GGUF formats specifically so quality holds up close to bfloat16 after quantization. For teams that would rather not manage GPUs, third-party inference host SiliconFlow lists the model at $0.10 per 1M input tokens and $0.30 per 1M output tokens (a blended $0.12 per 1M under a 7:2:1 cache:input:output ratio), though this is a single third-party price point, not Google's own hosted rate, since Google does not sell Gemma inference directly. Deployment options span Hugging Face, Kaggle, Ollama, LM Studio, vLLM, SGLang, MLX, and llama.cpp for local or self-managed serving, plus Google Cloud's Vertex AI Model Garden, Cloud Run GPU, and GKE for managed cloud deployment. AWS added Gemma 4 support to Bedrock in mid-June 2026. Google also ships an official multi-token-prediction (MTP) drafter model for Gemma 4 that adds speculative decoding, claiming up to 3x faster generation with no quality loss when paired with the 12B base model. The model shipped under a plain Apache 2.0 license, a meaningful change from the earlier custom "Gemma Terms of Use" that governed Gemma 1 through 3. That older license was broadly permissive but carried Google-specific use restrictions that made some enterprise legal teams hesitate; Apache 2.0 is the same OSI-approved license used by Qwen, Mistral, and most of the open-weight ecosystem, meaning commercial fine-tuning, redistribution, and paid hosting are unambiguously allowed with just attribution and license-text inclusion, no derivative-weight sharing required. Pre-training data has a cutoff of January 2025 and spans web documents, code, images, and audio across more than 140 languages, filtered for personal information, CSAM, and general safety and quality issues per Google's stated data-handling policy. Best fit is developers building on-device or self-hosted multimodal agents (browser automation, document pipelines, voice-plus-vision assistants) who want to avoid per-token API costs and keep data on their own infrastructure; teams that need Google's absolute strongest reasoning or agentic coding performance should look at Gemma 4 31B or Gemini 3.1 Pro instead, since the 12B trades some ceiling for a much smaller VRAM footprint.
Pricing
Gemma 4 12B is open-weight and free to self-host: Apache 2.0 license, no per-token fee, runs on an 8GB GPU at Q4_K_M quantization. Google does not sell its own hosted inference for Gemma. Third-party host SiliconFlow lists hosted API access at $0.10 per 1M input tokens and $0.30 per 1M output tokens (blended $0.12 per 1M), as of July 2026, for teams that would rather not manage GPUs.
Key Features
- Encoder-free multimodal input: A 35M-parameter linear embedder projects raw image patches and audio directly into the model, skipping the heavyweight vision/audio encoders most multimodal models rely on.
- 256K token context window: 262,144 tokens shared across the whole Gemma 4 family, enough for full codebases or long video transcripts on a 12B-class model.
- Runs on 8GB consumer GPUs: About 6.6GB VRAM at Q4_K_M quantization, with official Google QAT checkpoints that hold quality close to bf16.
- Native function calling: Structured tool-use support built in for agentic workflows, without a separate fine-tune or adapter.
- Video and audio understanding: Processes video as sampled frame sequences and accepts native audio input, alongside text and image, in one unified model.
Pros
- Free to self-host under a clean Apache 2.0 license, with no Google-specific usage restrictions left over from the older Gemma Terms of Use.
- Encoder-free architecture measurably reduces multimodal latency versus encoder-heavy rivals of similar size.
- Fits on an 8GB GPU or a 16GB unified-memory laptop thanks to official quantization-aware training checkpoints.
Cons
- No official SWE-bench, AIME or MMLU-Pro score published for the 12B; only third-party GPQA Diamond and DocVQA figures exist so far.
- Audio input only, no audio output, so voice assistants still need a separate TTS model.
- Meaningfully behind its own 31B sibling and closed frontier models like Gemini 3.1 Pro on ceiling reasoning tasks.
Benchmarks
- docvqa: 94.9
- gpqa diamond: 78.8
Frequently Asked Questions
What is Gemma 4 12B and who built it?
Gemma 4 12B is an open-weight multimodal language model built by Google DeepMind, released June 3, 2026 as the mid-sized member of the Gemma 4 family. It uses a dense, decoder-only Transformer with roughly 12 billion parameters and an encoder-free architecture that projects images and audio directly into the model rather than routing them through separate encoders. It reports 78.8% on GPQA Diamond and 94.9% on DocVQA according to independent write-ups, since Google has not published official 12B-specific reasoning benchmarks. The model sits below the 26B-A4B and 31B Dense variants in the Gemma 4 lineup, trading some ceiling reasoning for a smaller footprint that runs on an 8GB consumer GPU. It was designed to bring agentic, multimodal on-device intelligence to laptops rather than data centers. Context window is 262,144 tokens, shared across the whole family.
How much does Gemma 4 12B cost per 1M tokens?
Gemma 4 12B is free to self-host: it ships under Apache 2.0 with no per-token license fee, and it runs on roughly 6.6GB of VRAM at Q4_K_M quantization, fitting an 8GB consumer GPU. Google does not sell its own hosted inference endpoint for Gemma models. Third-party inference host SiliconFlow lists hosted API pricing at $0.10 per 1M input tokens and $0.30 per 1M output tokens, a blended $0.12 per 1M under a typical cache:input:output ratio, as of July 2026. A workload of 1M input and 200K output tokens through that hosted API costs about $0.16. Self-hosting on an owned RTX 4090 or M2/M3 MacBook Pro costs nothing beyond the hardware and electricity. Because it is open-weight, other hosts may list different rates; check the provider you actually use.
What is Gemma 4 12B's context window and max output?
Gemma 4 12B has a 262,144 token (256K) context window, the same figure shared across the entire Gemma 4 family from E2B up to 31B Dense. Google has not published a separate maximum output token limit distinct from the shared context budget in the model's public documentation. The large context window lets a 12B-class model handle full codebases, long PDFs, or hour-plus video transcripts (processed as sampled frames) without chunking the input. This matches or exceeds the context window of many larger proprietary models at a fraction of the parameter count. No independent needle-in-haystack recall evaluation at the full 256K depth was found as of this writing, so treat long-context recall quality as unverified rather than assumed. Compare this to Gemma 3's smaller context ceiling, a meaningful jump for the same size class.
How does Gemma 4 12B compare on benchmarks vs Gemma 3 and Llama 4?
Gemma 4 12B reports 78.8% on GPQA Diamond and 94.9% on DocVQA in third-party benchmarking, both notable jumps over Gemma 3's comparable-size tier on graduate-level reasoning and document understanding. Google has not published an official SWE-bench, AIME, or MMLU-Pro score for the 12B specifically, unlike its own 31B sibling which posts 89.2% on AIME 2026 and 80.0% on LiveCodeBench v6. Against Llama 4 Scout and Qwen3 14B, independent comparisons position Gemma 4 12B competitively on multimodal tasks (vision, audio, video) but without a definitive public coding benchmark to call a clear winner. Because several of these numbers come from independent blogs rather than Google's own model card, treat them as directional until Google publishes confirmed 12B figures. The clearest, vendor-confirmed differentiator is architecture (encoder-free) and context window (256K), not a specific benchmark score.
Is Gemma 4 12B open source or proprietary?
Gemma 4 12B is open-weight and licensed under Apache 2.0, a genuine change from the earlier custom Gemma Terms of Use that governed Gemma 1 through 3. Apache 2.0 is the same OSI-approved permissive license used by Qwen and Mistral, allowing commercial fine-tuning, redistribution, and paid hosting with just attribution and license-text inclusion, no requirement to share derivative weights or training data. Weights are hosted on Hugging Face as google/gemma-4-12B (base) and google/gemma-4-12B-it (instruction-tuned), plus official quantization-aware training checkpoints in Q4_0 unquantized and GGUF formats. VRAM requirements run about 6.6GB at Q4_K_M quantization, fitting an 8GB GPU, with the raw bf16 checkpoint needing considerably more. No commercial-use restrictions remain under the new license, unlike the earlier Gemma Terms of Use's carve-outs around critical infrastructure and minors.
What modalities does Gemma 4 12B support?
Gemma 4 12B accepts text, image, audio, and video input, and produces text and structured tool-call output; it does not produce audio output. Video is processed as sampled frame sequences rather than true continuous video understanding. Interleaved multimodal prompts, mixing text and images freely within one prompt, are supported natively rather than through a separate API mode. Capabilities include object detection, document and PDF parsing, screen and UI understanding, chart reading, multilingual OCR, and handwriting recognition. Function calling and structured output are built in for agentic tool-use workflows, without needing a separate fine-tune. Audio input specifically is limited within the family: only the E2B, E4B and 12B checkpoints support it, while the larger 26B-A4B and 31B Dense variants do not.
Does Gemma 4 12B train on user data?
Because Gemma 4 12B is open-weight and self-hosted (or served by third-party hosts, not Google directly), there is no single Google-operated API that could retain or train on your inputs. If you self-host, your data never leaves your own infrastructure. If you use a third-party host like SiliconFlow, that host's own data retention and training policy applies, not Google's, so check that provider's terms directly. Google's own pre-training data cutoff for the model itself is January 2025, drawn from web documents, code, images and audio across 140+ languages, filtered for personal information, CSAM, and quality/safety issues. There is no formal SOC 2, HIPAA, or GDPR certification tied to the model weights themselves, since compliance obligations fall on whichever organization deploys and serves the model. The Apache 2.0 license carries no data-handling terms of its own.
Who is Gemma 4 12B best for and who should avoid it?
Gemma 4 12B is best for developers building on-device or self-hosted multimodal agents (document pipelines, screen and chart understanding, voice-plus-vision assistants) who want to avoid per-token API costs and keep data on their own hardware. It suits teams fine-tuning a permissively-licensed base model under Apache 2.0 without derivative-sharing obligations, and startups that need multimodal capability without a Vertex AI or OpenAI bill. Avoid it if you need Google's absolute strongest reasoning or agentic coding performance, since its own 31B sibling scores meaningfully higher on published benchmarks like AIME 2026. Also avoid it for voice-to-voice assistant use cases, since it accepts audio input but produces no audio output, requiring a separate text-to-speech model. Enterprise teams that need a vendor-backed SLA and dedicated support contract should look at a managed frontier model instead, since Gemma ships as weights with community and Google Cloud tooling support, not a first-party support agreement.