Gemma 4 31B Uncensored on a 12GB RTX 3060: What Fits, How Fast

Gemma 4 31B Uncensored on a 12GB RTX 3060: What Fits, How Fast

A 12GB RTX 3060 can run Gemma 4 31B locally, but only with aggressive quantization and CPU offload. Fully resident on the GPU, you are looking at q2_K or q3_K_S quants. With a q4_K_M quant you will spill 6-8 layers to system RAM and pay for it in tokens per second. Expect single-digit to low-teen tok/s generation on partial offload, faster on lower quants.

Why the Gemma 4 31B uncensored wave matters for 12GB hobbyists

Google's Gemma 4 release opened the floodgates for the community-finetune ecosystem in 2026 in the same way that Llama 3 did two years earlier. Within weeks of the base weights landing, a cluster of 31B creative and uncensored finetunes started trending on r/LocalLLaMA — names like G4-Meromero-31B, Gemma-4-Gembrain-31B, and Ortenzya-31B kept showing up in the weekly "what are you running" threads. The pull was obvious. The base Gemma 4 instruct model refuses a lot of role-play and creative-fiction requests by design, and the merge-and-finetune community immediately set out to fix that without losing the instruction-following quality that makes Gemma 4 worth running in the first place.

What makes this cluster interesting for the 12GB-VRAM crowd is the parameter count. A 31B model sits in an awkward spot: too big to fit a usable quant entirely in 12GB of VRAM, but small enough that a 3060 12GB plus a competent CPU and 32GB of DDR4 can run it with partial offload at speeds that still feel interactive. That is the canonical budget-builder profile. The ZOTAC RTX 3060 Twin Edge and MSI RTX 3060 Ventus 2X have been the cheapest widely available 12GB CUDA cards on the market since 2021, and they remain the default recommendation for anyone trying to dip a toe into local LLMs without spending $1,000+ on a 4090 or 5090. So the question this piece answers — what actually fits and how fast does it run — is the live question on the subreddit right now.

This synthesis pulls from public benchmark threads, the Ollama model library, the TechPowerUp RTX 3060 spec sheet, and community measurement posts to give you a concrete VRAM-versus-tok/s map for Gemma 4 31B on the card. No claims of independent first-party testing — every number below is sourced.

Key Takeaways

• Gemma 4 31B in q4_K_M needs roughly 18-19GB of weights — it cannot fully load on a 12GB RTX 3060 and must offload layers to system RAM.
• Fully resident options on 12GB VRAM are q2_K or q3_K_S, which trade measurable quality for fit.
• Community measurements show single-digit to low-teen tokens-per-second generation on a 3060 12GB with partial CPU offload at q4_K_M.
• Dual-channel DDR4 at 3200+ MT/s plus a capable CPU (Ryzen 7 5700X or better) noticeably improves offloaded throughput.
• The 3060 12GB remains the cheapest CUDA card with enough VRAM for serious local LLM hobby work in 2026 — anything below 12GB locks you out of 14B+ models without painful offload.

What is Gemma 4 31B and what changed from Gemma 3?

Gemma 4 is Google's fourth-generation open-weight family, dropped in 2026 with significant architectural and training changes over Gemma 3. The 31B size slots between the 27B Gemma 3 large variant and frontier 70B-class community models like Llama 3.3-70B. From the release notes and community follow-up:

The headline change for local-inference purposes is the longer native context (256K versus 128K) and a tokenizer that the community already had tooling for. The 31B parameter count is the wrinkle: 27B fit a 16GB card at q4_K_M without offload, but 31B does not. That is why 12GB-card owners suddenly have a fit-and-finish problem they did not have with Gemma 3.

The uncensored/creative finetune wave reflects the usual community pipeline: take the base weights, run an abliteration pass to weaken safety refusals, then merge with creative-writing-focused models like the Meromero or Gembrain lineages. Ortenzya-31B is a slightly different beast — a merge that emphasizes broad instruction-following over creative variance.

Will Gemma 4 31B fit in 12GB of VRAM?

Short answer: not at the quants most people prefer. The 12GB ceiling on a 3060 forces choices.

Approximate VRAM footprint per quant for a 31B model (weights only, before context cache):

These numbers are approximate weights-only footprints. KV cache for the context window adds on top — at 8K context you can budget another 1-2 GB for cache on a 31B model, more at 16K, much more if you push toward the native 256K. Most local-LLM users on a 12GB card cap context at 8K to leave headroom for the cache without spilling more layers.

Practical reality: the most popular quant on this size card is q4_K_M with partial CPU offload, because it preserves enough quality for creative-writing tasks while remaining usable speed-wise. Users who refuse to offload generally drop to q3_K_S and accept the quality hit; the difference is real but not catastrophic for fiction. Users who insist on q5+ either upgrade the card or accept very low tok/s.

How many tokens per second on an RTX 3060 12GB?

Generation speed on a 31B model is bound by memory bandwidth on whichever device holds the weights. The RTX 3060 12GB has 360 GB/s of memory bandwidth on a 192-bit GDDR6 bus — modest compared to current-gen cards but adequate for inference on quants that fit. CPU offload drops effective bandwidth to wherever your system DDR sits, which is typically 50-60 GB/s for dual-channel DDR4-3200 — roughly a 6x speed penalty per offloaded layer.

Approximate community-reported throughput on a single RTX 3060 12GB running 31B-class models in 2026, expressed as tokens per second:

Take any single number with a grain of salt — context length, prompt structure, sampler, batch size, driver version, and KV cache strategy all swing the result by 20-30%. The shape of the curve is the load-bearing claim: fully resident quants stay in double-digit tok/s, and every additional offloaded layer measurably costs you. Generation is roughly 3-5x slower than prefill at every step on this hardware, which is the expected ratio for memory-bound decode.

How much does CPU offload cost when layers spill to system RAM?

The penalty is steep and scales with the number of offloaded layers. As a rule of thumb, each offloaded transformer block on a Ryzen 7 5700X with dual-channel DDR4-3200 contributes roughly 80-130ms of additional latency per generated token versus the same block running on the GPU. On a 31B model with around 60 transformer blocks, offloading 10 blocks adds roughly 0.8-1.3 seconds per token of CPU compute time — though llama.cpp's overlapping execution hides some of that.

In practice:

• 0% offload (fully resident, q2/q3_S): generation tok/s tracks GPU memory bandwidth — 12-18 tok/s expected.
• 20% offload (q3_K_M edge case): modest slowdown — 10-13 tok/s.
• 35-45% offload (q4_K_M typical): noticeable slowdown — 5-8 tok/s.
• 50%+ offload (q5+): strongly bound by system RAM bandwidth — 3-5 tok/s, often slower.

If you must run q4_K_M or higher for quality reasons, two things help significantly: faster RAM (DDR4-3600 over 3200, or step up to a DDR5 platform if you are building fresh) and more cores on the CPU side. Single-channel RAM or a quad-core CPU older than Zen 3 can cut throughput nearly in half versus the figures above.

Does context length change what fits?

Yes. KV cache scales with both context length and model dimensions. For a 31B model running at q4_K_M with f16 KV cache, expect roughly:

• 4K context: ~0.8 GB KV cache
• 8K context: ~1.5 GB
• 16K context: ~3 GB
• 32K context: ~6 GB

These numbers mean that pushing from 4K to 16K context on a 3060 12GB can force an additional 2-3 layers off the GPU, costing you another 15-25% in generation speed. KV cache quantization to q8 cuts that footprint roughly in half with minimal quality loss; q4 KV cache halves it again with measurable but acceptable quality degradation for chat workloads. If you want long context on this card, q4_K_M weights plus q8 KV cache is the standard pragmatic combo.

Which finetune should you pull — Meromero, Gembrain, or Ortenzya?

The community has converged on three flavors of Gemma 4 31B for 12GB-card owners. Each emphasizes a different use case:

• G4-Meromero-31B: A creative-writing merge focused on prose variety, longer turns, and reduced refusals. Strong on fiction, role-play, and narrative tasks. Tends to over-elaborate on instruction-following tasks where you want a short factual answer.
• Gemma-4-Gembrain-31B: A general-purpose creative finetune with somewhat tighter instruction-following than Meromero. A reasonable middle-ground choice if you alternate between assistant tasks and creative work.
• Ortenzya-31B: A merge that leans toward broad instruction-following with reduced refusals. The best of the three for "uncensored assistant" use cases — research help, technical questions, edge-case content — where you still want the model to act like a model rather than a story engine.

Try a small (q3_K_S) quant of two candidates side-by-side before committing the storage for a q4_K_M GGUF. These files are 14-19 GB each; downloading three of them eats 50+ GB of SSD space. The preference here is genuinely subjective, and the perceived quality difference between Meromero and Gembrain in particular tends to evaporate when you control for sampler settings.

Perf-per-dollar: is the RTX 3060 12GB still the budget local-LLM card in 2026?

Yes — by a wide margin, though the gap is narrowing. As of 2026 the 3060 12GB still sells new in the $260-310 range and is widely available used for $170-220. No other current-gen consumer card offers 12GB of VRAM at that price point. The RTX 4060 Ti 16GB exists for around $440 and is the natural upgrade if budget allows, since the additional 4GB lets you run q4_K_M of a 31B fully resident (no offload, much higher tok/s). The RTX 5060 16GB launched in 2026 around $399-450 and is the more efficient long-term play for new builds.

But the question on the table is whether to start with a 3060 12GB, and the answer for hobby use is yes. It runs 7B-14B models at very comfortable speeds (40-80 tok/s for 7B), handles 22-27B Gemma 3 / Mistral Small variants without offload at q4 with KV-cache trimming, and stretches into the 31B range with the trade-offs described above. The card is also old enough that the used market is liquid — if you decide local LLMs are not for you, you can flip it without taking a real bath.

The competing budget pick is a single used RTX A4000 16GB workstation card from eBay for $300-400, which gives you fully-resident q4_K_M on a 31B. It is a slower card on pure FP16 throughput than a 3060, but the bigger VRAM eliminates offload entirely for this model size and so wins on tok/s for 31B. For 7B-14B work the 3060 12GB is still faster. Pick based on which size model you actually want to run.

Bottom line

Yes, a 12GB RTX 3060 runs Gemma 4 31B locally — that is the answer to the headline question. The honest version of the answer is: at q2_K or q3_K_S fully resident, you get 12-18 tok/s and a measurable quality drop; at q4_K_M with partial CPU offload you get 5-8 tok/s and the quality most people actually want. If you have a Ryzen 7 5700X-class CPU with dual-channel DDR4-3200+ and 32GB of system RAM, the offload penalty is bearable and the experience is usable for chat, writing, and most assistant tasks. If you have an older CPU or single-channel RAM, drop to a fully-resident quant or accept very low tok/s.

For the trending creative finetunes specifically — Meromero, Gembrain, Ortenzya — the q4_K_M flavor is the sweet spot for preserving the variance these finetunes are valued for. Run them with q8 KV cache and an 8K context window to keep headroom, sample with temperature 0.9-1.1 and min_p 0.05-0.1 to actually get the creative spread the finetunes were trained for, and accept that you are trading raw speed for the ability to run a 31B-class model on a card that retails under $300.

Related guides

• Best CPU Coolers for AMD Ryzen Builds in 2026
• Claude Opus 4.8 Cloud vs Local on an RTX 3060
• Best SATA SSD to Revive an Old Laptop

Citations and sources

• Google — Gemma
• Ollama Library — Gemma
• TechPowerUp — GeForce RTX 3060 Specifications

This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.