Gemma 4 on RTX 3060: 12B Fit, Speed & Setup Guide

Quick answer: The 12GB RTX 3060 runs Gemma 4 12B comfortably at Q4 quantization — real-world VRAM use lands around 7–8.6 GB, leaving 3–5 GB free for context. It also runs E4B with room to spare. The 26B A4B model is a different story: its 15.6 GB minimum footprint doesn't fit in 12GB without CPU offloading, which brings a real speed penalty. If you have the 8GB RTX 3060 Ti or a laptop RTX 3060 (6GB), skip to the caveat section below — those are meaningfully more constrained cards, despite sharing the "3060" name.

The RTX 3060 12GB has been the budget local-AI card of choice since it launched, for one simple reason: Nvidia gave a sub-$300 GPU more VRAM than some $700 cards get. That decision is paying off again with Gemma 4.

Which Gemma 4 Models Fit in 12GB?

These are the same baseline numbers from Google's official documentation, covered in full in our Gemma 4 hardware requirements guide. The practical detail that matters here: Q4_0 weights for the 12B model are listed at 6.7 GB, but that's weights only — add the KV cache and runtime overhead and real-world usage climbs to roughly 7–8.6 GB, depending on context length and exact quant (Q4_K_M vs Q5_K_XL vs others). Either way, a 12 GB card has 3–5 GB left over, which is enough for a solid chunk of context and normal desktop overhead at the same time.

26B A4B is the one to watch out for. It's a Mixture-of-Experts model — only about 4B parameters activate per token, but all 26B worth of experts have to sit in memory because the router needs instant access to any of them. At a 15.6 GB minimum, it simply doesn't fit inside 12 GB of VRAM. You can force it to run with CPU offloading, but don't expect it to feel like a 12GB-native model — see the offloading section below.

Real-World Speed: What Tokens/Sec Looks Like

Here's a sourced data point rather than a guess: a community benchmark posted to X by @ItsmeAjayKV (summarized by note.com/zephel01) ran Gemma 4 12B at Q5_K_XL on an RTX 3060 12GB using llama.cpp with CUDA, Flash Attention, and Q8_0 KV cache quantization — no speculative decoding — and measured:

• ~33.3 tokens/second generation speed
• ~1,152 tokens/second prompt processing (prefill) at 4K context

For comparison, community reports on an RTX 4060 8GB (a newer but lower-bandwidth card) put standard Q4_K_M generation at ~21 tokens/second (buildfastwithai.com, techsy.io). That gap tracks with raw memory bandwidth: the RTX 3060 12GB's 360 GB/s beats the RTX 4060's narrower 128-bit bus, and LLM decoding is bandwidth-bound far more than it's compute-bound. In practice, the older RTX 3060 punches above its "budget card" reputation here — this is one case where more VRAM and a wider memory bus beat a newer chip.

Labeled estimate: if you enable speculative decoding with Gemma 4's included draft model (llama.cpp's --spec-type draft-mtp flags), expect a meaningful bump over the ~33 tok/s baseline — one X post from @analogalok reported 20+ tok/s decode and 700+ tok/s prefill on an RTX 4060 using this setup, and a 3060 should do at least as well given its bandwidth edge. This hasn't been independently confirmed on a 3060 specifically, so treat it as directional.

E4B, being smaller and easily GPU-resident, should run noticeably faster than either of these 12B figures — expect it to feel closer to instant for short responses.

What Happens When a Model Doesn't Fit: CPU Offloading

If you try to push 26B A4B onto a 12GB RTX 3060, llama.cpp (and Ollama, which wraps it) will offload whatever doesn't fit in VRAM to system RAM and run those layers on the CPU. Two flags control this directly:

• -ngl (--n-gpu-layers) sets how many transformer layers load onto the GPU. Lower it, and more of the model runs on CPU.
• --n-cpu-moe is MoE-aware: it lets you specifically keep expert (FFN) tensors on the CPU while attention and routing stay on the GPU. Because only ~4B parameters activate per token in 26B A4B, this hurts throughput less than offloading a dense model of the same size would — but it's still a real, felt slowdown, not a rounding error.

The general pattern documented across 8GB-class GPU setups running Gemma 4 12B (knightli.com, xda-developers.com) is a genuine speed cliff: users had to drop from 48 GPU layers to 28 and cap context around 4K–20K tokens just to keep things stable, well below what a model that fully fits delivers. Applied to 26B A4B on a 12GB 3060, expect the same shape of trade-off — it will load and generate output, but noticeably slower and more memory-fragile than 12B, which fits natively. If 26B A4B is your goal, a 16GB+ card is the more comfortable path.

Setup: Getting Gemma 4 Running on Your RTX 3060

The fastest path is Ollama, which wraps llama.cpp and handles quantization and GPU detection for you:

ollama pull gemma4:12b
ollama run gemma4:12b

For the full install walkthrough — including image input, the REST API, and picking between variants — see our guide to running Gemma 4 with Ollama. If you want more manual control over quantization and offload flags (useful if you ever do try 26B A4B), llama.cpp directly gives you access to -ngl, --n-cpu-moe, and KV cache quantization settings the Ollama CLI doesn't expose.

When to Upgrade: The Used RTX 3090 Question

If you're finding yourself wanting 26B A4B or 31B regularly rather than occasionally, the RTX 3060 12GB isn't the card for that — you're CPU-offloading a model that a 24GB card would run natively. The common upgrade path here is a used RTX 3090 (24GB), which comfortably fits 26B A4B and runs 31B at Q4_0 (17.4 GB) with headroom to spare.

Pricing on used 3090s has been volatile through 2026 — tracker sites showed listings anywhere from roughly $700 to $1,900+ depending on condition, cooler design, and seller type as of early July 2026 (bestvaluegpu.com, resaleprices.com). That's a wide enough range that it's worth checking current local listings rather than budgeting off any single number — but even at the higher end, it's a fraction of a new 24GB card, and 24GB is genuinely the next meaningful step up for Gemma 4's larger models.

If 12B already covers what you need, there's no upgrade pressure — it's the model this card was practically built for.

FAQ

Can the RTX 3060 run Gemma 4 12B? Yes. At Q4 quantization, real-world VRAM use is roughly 7–8.6 GB on a 12GB card, leaving several gigabytes free for context and background processes. Community benchmarks show around 33 tokens/second at Q5_K_XL.

Can the RTX 3060 run Gemma 4 26B A4B? Not natively — 26B A4B needs 15.6 GB minimum at Q4_0, more than the 12GB card has. It can run with CPU offloading (--n-cpu-moe in llama.cpp), but expect a real speed penalty. A 16GB+ GPU is the more comfortable option for this model.

Does the 8GB RTX 3060 Ti run Gemma 4 the same way? No — treat it as a different card. The RTX 3060 Ti has 8GB of VRAM, not 12GB, so 12B's ~7–8.6 GB real-world footprint leaves very little room for context; you'll likely need to trim context length or lightly offload to stay stable. E4B is a safer default there. Same caution applies to laptop RTX 3060 GPUs, which ship with only 6GB — closer to an E4B-only card for Gemma 4 than a 12B one.

Next Steps

For the full picture across every Gemma 4 model and every hardware tier — Mac, other NVIDIA cards, and CPU-only setups — see the Gemma 4 hardware requirements guide. If your card is an 8GB 3070 or a 10–12GB 3080 instead, we cover that VRAM class specifically in Gemma 4 on RTX 3070/3080.