Gemma 4 on RTX 3070 & 3080: VRAM Fit and Speed Guide

Quick answer: The RTX 3080 (10GB or 12GB) runs Gemma 4 12B comfortably and is meaningfully faster than a 3060. The RTX 3070 and 3070 Ti, both stuck at 8GB VRAM, are the more interesting case: they're faster cards on paper, but 8GB is a tighter squeeze for 12B than the 12GB RTX 3060's budget-friendly memory pool — you may need reduced context or CPU offloading to keep things stable. VRAM capacity, not raw horsepower, is the bottleneck that decides whether these cards feel fast or frustrating.

This page covers four cards across two VRAM classes: the 8GB RTX 3070 / 3070 Ti, and the 10–12GB RTX 3080 / 3080 Ti. If you're on a 12GB RTX 3060 instead, see our dedicated RTX 3060 guide — that budget card actually has more headroom for Gemma 4 12B than the 8GB cards covered here, despite being the "lower tier" part on paper.

The Fit Table: 8GB vs 10–12GB

These fit numbers are consistent with the baseline from our Gemma 4 hardware requirements guide: 12B needs 6.7 GB just for Q4_0 weights, and real-world usage — weights plus KV cache plus runtime overhead — lands around 7–8.6 GB depending on context length and quant variant. On an 8 GB card, that's essentially the entire VRAM budget before you've had a conversation. On a 10 GB+ card, it's a comfortable fit with room left over.

The Honest Tension: Faster Chip, Tighter Memory

Here's the part that trips people up. Judged purely on memory bandwidth, the RTX 3070 (448 GB/s) and 3070 Ti (608 GB/s) both outclass the RTX 3060 12GB (360 GB/s) — and LLM token generation is largely bandwidth-bound, so a fully GPU-resident model runs faster on either 3070 variant than on a 3060. But "fully GPU-resident" is the catch: Gemma 4 12B's real-world 7–8.6 GB footprint barely fits — or doesn't fit — inside 8 GB once you account for context and overhead, while it fits with 3–5 GB to spare on the 3060's 12 GB pool.

The practical result: a 3070 that has to partially offload layers to system RAM to stay stable can end up slower in practice than a 3060 running the same model fully on GPU, even though the 3070's raw silicon is faster. VRAM capacity, not compute throughput, is the variable that decides whether you get the card's real speed or a CPU-offload penalty on top of it.

The 3080 and 3080 Ti sidestep this entirely — 10 GB and 12 GB are both enough for 12B to load natively, so you get the full benefit of their considerably higher bandwidth (760–912 GB/s) with none of the offload tax.

Real-World and Estimated Speed

The only precisely sourced Gemma 4 12B benchmark on record for this hardware class comes from a community test on a 12GB RTX 3060: ~33.3 tokens/second generation and ~1,152 tokens/second prefill at Q5_K_XL, using llama.cpp with Flash Attention and Q8_0 KV cache — reported via X user @ItsmeAjayKV, summarized on note.com/zephel01. A separate community report on an RTX 4060 8GB put standard Q4_K_M generation at roughly ~21 tokens/second (buildfastwithai.com).

Neither the RTX 3070, 3070 Ti, 3080, nor 3080 Ti has a publicly reported Gemma 4 12B tokens/sec figure as of this writing — so the following are labeled estimates, scaled from the sourced 3060 figure using each card's memory bandwidth relative to the 3060's 360 GB/s (LLM decode is bandwidth-bound, so this is a reasonable first-order approximation, not a guarantee):

Treat these as directional, not benchmarked. The 3080/3080 Ti figures are more trustworthy since those cards don't hit the offload penalty that complicates the 3070/3070 Ti picture.

When 12B Doesn't Fit: CPU Offloading on 8GB Cards

If you're on a 3070 or 3070 Ti and 12B won't stay comfortably on GPU at the context length you want, llama.cpp (and Ollama underneath) falls back to splitting the model between VRAM and system RAM. Community documentation of this exact scenario on 8GB-class GPUs (knightli.com, xda-developers.com) shows the shape of the trade-off clearly: users reduced -ngl (GPU layers) from 48 down to 28, and capped context around 4K–20K tokens, to keep the model stable without running out of memory.

Two practical levers if you're in this spot:

• Lower -ngl gradually rather than guessing — start high, back off if you hit out-of-memory errors, and expect a real (not marginal) speed drop for every layer pushed to CPU.
• Drop context length before dropping GPU layers. KV cache growth is often what pushes an "almost fits" setup over the edge; capping context at 4K–8K tokens frequently rescues a configuration that OOMs at 32K.

If you find yourself tuning these flags every session, that's the signal the 8GB ceiling is the real constraint — not the GPU's compute — and a 10GB+ card (or an E4B fallback for that machine) is the more durable fix than continued flag-tweaking.

Setup

Ollama is still the fastest path to a running model on any of these cards:

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

If you're on an 8GB 3070/3070 Ti and need manual control over -ngl, context length, or KV cache quantization, llama.cpp directly gives you access to flags Ollama's CLI doesn't expose. See our guide to running Gemma 4 with Ollama for the full setup walkthrough, including image input and the REST API.

FAQ

Can the RTX 3070 run Gemma 4 12B? It can load, but 8GB is a tight fit for 12B's real-world 7–8.6 GB footprint. Expect to run with a shorter context window or some CPU layer offloading rather than the fully-comfortable fit you'd get on a 10GB+ card.

Is the RTX 3080 fast enough for Gemma 4 12B? Yes — both the 10GB and 12GB (Ti) variants fit 12B comfortably and offer 2–2.5x the memory bandwidth of a 3060, translating to meaningfully faster generation with none of the 8GB cards' offload penalty.

Is a 3070 or 3060 12GB better for Gemma 4? For 12B specifically, the 3060's 12GB gives it more comfortable headroom than the 3070's 8GB, even though the 3070 has higher raw bandwidth. If 12B is your main target model, the extra VRAM matters more here than the extra speed.

Next Steps

For every Gemma 4 model against every hardware tier — including Mac, CPU-only setups, and the 12GB RTX 3060 — see the Gemma 4 hardware requirements guide and our RTX 3060 breakdown.