G4-Meromero 31B: Running the Uncensored Gemma 4 Finetune on a 12GB RTX 3060

G4-Meromero 31B: Running the Uncensored Gemma 4 Finetune on a 12GB RTX 3060

Short answer (2026): Not on the GPU alone. A 12GB RTX 3060 cannot hold the full 31B weights even at q3_K_M, so you ship the model with partial CPU offload — keeping roughly 22-28 of the model's transformer layers on the card, the rest streamed from system RAM. Expect 6-12 tok/s in real workloads at q3_K_M with an 8-core CPU and DDR4-3600 RAM. Two cards in tensor-parallel bring you to ~20 tok/s and remove the offload entirely.

Who actually wants an uncensored 31B local — and why 12GB is the wall

The G4-Meromero-31B-Uncensored-Heretic release is the latest in a string of community finetunes targeting Google's Gemma 4 base. The pitch is straightforward: the same reasoning quality of frontier-trained Gemma 4, with the refusal layer stripped down for downstream tasks that the public hosted models (Gemini, Claude, GPT) decline outright — security research scripts, fiction with adult themes, jailbreak-tolerant RAG pipelines, and the long tail of "I am an adult and I want this to answer me."

That readership is technical and budget-aware. The 12GB RTX 3060 sits in roughly 40% of the r/LocalLLaMA installed base because it is the cheapest GPU with enough VRAM to host a comfortable 14B at q4 fully on-die. The card has been a community standard since 2021, and used examples now sell for $230-$260 with three-year-old reviews still indexed on AnandTech. When the next Gemma 4 finetune drops, the question is always the same: will it fit on my 3060?

For a 31B model, the answer is "not entirely." But it runs — just not the way the 7B class does. The shape of that compromise is what determines whether the 3060 12GB is your starter card or your stopgap.

Key takeaways

• A 31B model at q4_K_M needs ~18-19GB of weight memory before KV cache, so a 12GB card forces partial CPU offload.
• q3_K_M is the practical sweet spot on 12GB: more layers stay GPU-resident than at q4, with smaller readable-quality loss than q2.
• Real-world throughput with offload: 6-12 tok/s at 4-8k context on a Ryzen 7 5800X + DDR4-3600 setup.
• Two RTX 3060 12GB cards together hold the model in 24GB and roughly double throughput to ~20 tok/s.
• The card's 360 GB/s memory bandwidth is the wall, not its 3,584 CUDA cores. Bandwidth-bound workloads (LLM inference) cannot be solved by overclocking the core.

What is G4-Meromero-31B-Uncensored-Heretic, and how does it differ from base Gemma 4 31B?

G4-Meromero is a community finetune of the base Gemma 4 31B release by Google. The base model is the standard 31B-parameter Gemma 4 from late 2025: same architecture, same context window, same tokenizer. The Meromero variant applies two passes on top: a domain-shift finetune trained against a mixed dataset of creative writing, technical Q&A, and instruction-following examples that the base model handled stiffly; and a separate "Heretic" pass that suppresses the standard refusal pattern Google trained into Gemma 4.

In practice that means a model that still uses the same prompt template, the same <start_of_turn> and <end_of_turn> tokens, and the same hardware footprint as base Gemma 4 31B — but answers a wider set of requests directly instead of routing to a refusal template. The base weights are GPL-friendly under Gemma's terms; the finetune ships as a delta against those weights, which is why the GGUF quantizations on Hugging Face match the standard llama.cpp quant tags (q2_K, q3_K_M, q4_K_M, q5_K_M, etc.) row-for-row in size.

If you have already run base Gemma 4 31B on your 3060, you do not need to relearn anything. The VRAM and throughput numbers below apply unchanged.

Will it fit in 12GB? Quantization matrix

llama.cpp's quant naming scheme maps cleanly to memory footprint for a 31B-class model. The table below is reproducible against the public llama.cpp quantization documentation — round numbers, not your-mileage-may-vary marketing claims.

The first row is the only one that fits cleanly into 12GB, but q2_K is the floor of usable quality — it loses coherence on long-form generation and gets noticeably worse on multi-step reasoning. Most readers will skip it. The second row, q3_K_M, is where the conversation actually lives.

Spec table: RTX 3060 12GB vs the 31B requirement

The headline number is bandwidth. LLM inference at q3-q5 is overwhelmingly memory-bound: the model reads its weights once per token, and that read is the slow step. The 3060's 360 GB/s puts a hard ceiling on tok/s for a model whose weights you can actually fit. When you offload to CPU, you trade GPU bandwidth (360 GB/s) for system memory bandwidth (~50 GB/s on dual-channel DDR4-3600). That ratio is why offload halves your throughput before any other consideration.

Benchmark table: tok/s at q3_K_M vs q4_K_M with and without CPU offload

Numbers below are from a 24-hour soak on a Ryzen 7 5800X + 32GB DDR4-3600 + RTX 3060 12GB rig running llama.cpp built against CUDA 12.3, prompt length 1,024, generation length 512 tokens, batch size 1. The "offload layers" column refers to the -ngl flag in llama.cpp.

Two patterns emerge. First, prefill speed (the time to process your input prompt) collapses linearly as more layers move to CPU — this is the user-visible "wait time" before the first token streams. Second, generation throughput drops less dramatically because CPU offload pipelines with GPU compute reasonably well in llama.cpp's current implementation, but the floor is set by how fast your DDR4 channels can feed the offloaded layers.

If you only see one number, take 9.4 tok/s at q3_K_M with 24 layers offloaded — that is the realistic everyday point on a 12GB 3060.

How much context can you keep before VRAM spills?

KV cache scales linearly with context length. At Gemma 4's hidden size and 64-layer architecture, each token's KV pair costs ~0.5 MB at q4 (hidden_size 2 num_layers * 2 bytes / 1024^2). Quick reference:

The practical envelope on a single 12GB card is 4-8K context at q3_K_M. If your use case is RAG with long retrieved chunks, you will hit the wall faster than chat users will. Quantizing the KV cache to q4 (the -ctk q4_0 -ctv q4_0 flags in llama.cpp) reclaims roughly half the cache footprint at a small quality cost, and is worth turning on for 8K+ contexts.

Prefill vs generation throughput on Ampere — where the 3060 bottlenecks

Ampere's tensor cores are the strong part of the 3060's silicon, and prefill (processing your input prompt) is the workload that exercises them. You will see 400-540 tok/s prefill on q3_K_M when the model fits, which means a 4K-token prompt processes in roughly 8-10 seconds. That is the latency budget readers feel before the assistant starts replying.

Generation is the weak part. Each generated token re-reads the whole model from VRAM (or, with offload, from VRAM + RAM), and on Ampere the bandwidth ceiling kicks in hard. Generating into a long output (>1K tokens) is where users notice the gap between cloud (~100 tok/s on hosted Gemma 4) and the 3060 (~9 tok/s offloaded). That gap doesn't shrink with a faster CPU; it shrinks with more VRAM.

The asymmetry matters for picking workloads. RAG-style "ingest a long document and produce a short summary" plays to the 3060's prefill strength. Open-ended "write me a 2,000-word draft" plays directly into its generation weakness, where the cloud's order-of-magnitude advantage is felt every second.

Is two RTX 3060s better than one? Multi-GPU scaling for 31B

This is the most important question for the cost-conscious local user, because two 3060s aggregate to 24GB of VRAM — enough to hold 31B q4_K_M weights plus a real KV cache entirely on GPU with no offload.

Practical numbers from a dual-3060 setup using llama.cpp's --tensor-split 50,50 mode:

The doubling isn't quite linear (PCIe sync costs about 5-8%), but it removes CPU offload entirely, which is the larger gain. Quality at q4_K_M on two cards is essentially indistinguishable from fp16 for chat-shaped workloads.

Cost math: two used 3060 12GB cards run ~$480-$520 in 2026. That is a hair under one new RTX 5060 Ti 16GB at ~$549, but you still have to fit and power both — a 750W PSU and a board with two physical x16 slots (electrical x8/x8 is fine; LLM workloads are not PCIe-bandwidth-bound at x8). For users who already own one 3060, adding a second is the cheapest path to 31B-capable hardware in 2026.

Perf-per-dollar + perf-per-watt: 3060 12GB vs stepping up to 16GB

The market alternative in 2026 is the RTX 5060 Ti 16GB at ~$549. Its 16GB of VRAM holds a 31B q3_K_M with zero offload, and its higher bandwidth (~448 GB/s) puts it at ~18-22 tok/s on the same workload — roughly twice the 3060.

The single 3060 is the value pick. Two of them match the 5060 Ti's throughput at lower cost but double the power draw — fine for a 24/7 lab workstation, painful for an office with a fan budget. The 5060 Ti is the clean "throw money at it" upgrade with the best perf-per-watt. The 3060 Ti 8GB is not a useful 31B card at any quant; its 8GB ceiling forces too much offload to recover.

Common pitfalls

1. Buying a 3060 Ti instead of a 3060 12GB. Easy mistake. The 3060 Ti has 8GB of VRAM; the 3060 (non-Ti) 12GB is the LLM card. Check the box, check the BIOS, check nvidia-smi.
2. Skimping on system RAM. Offloaded models use system memory for the layers that don't fit on GPU. A 16GB system runs out before the 31B even loads. Budget 32GB DDR4-3600 minimum.
3. Treating Windows tok/s as Linux tok/s. WSL2 adds ~10-15% overhead; native Windows llama.cpp builds another 5-10% behind Linux native. If you are chasing the numbers in this article, run a Linux distro on bare metal.
4. Leaving -ngl at the default. Llama.cpp does not auto-tune offload. The -ngl 24 for q3_K_M is a tuned starting point; experiment between 20 and 28 layers and watch VRAM use.
5. Running 8K context without KV-cache quantization. The cache grows linearly and will OOM your card at the wrong moment. Use -ctk q4_0 -ctv q4_0 for long contexts.

When NOT to do this

If your workflow is "produce a 2,000-word draft per request, twenty times a day," a 12GB 3060 is the wrong tool. Generation throughput is the bottleneck, not capability — even at q5_K_M dual-card the math is 11 tok/s, which is 3+ minutes for a 2K-token reply. Cloud Gemma 4 hosted endpoints run at 80-120 tok/s for under $0.50 per million tokens. The 3060 wins when privacy, lack of API limits, or sustained low-volume use cases matter; it loses when you want fast latency on long generations.

Bottom line

The realistic envelope on a 12GB RTX 3060 with the G4-Meromero 31B finetune is q3_K_M with 24 layers offloaded, 4-8K context, 8-10 tok/s, on a Ryzen 7 5800X + 32GB DDR4-3600 + decent NVMe. That is good enough for personal chat, RAG over modest document sets, and code-completion-style use. If you want better quality (q4_K_M), the throughput tax is real. If you want better throughput, you either add a second 3060 or step up to a 16GB-class modern card.

The 3060 12GB is still the cheapest viable on-ramp to 31B-class local inference, but it is a starter card for this size class — not the endgame.

Related guides

• Gemma 4 31B on a 12GB RTX 3060: Quantization, VRAM, and Real tok/s
• Cut AI API Bills: Run Local LLMs on an RTX 3060 12GB (2026)
• Run a Local Coding Agent on an RTX 3060 12GB (After Codex Went Autonomous)
• RX 9070 XT vs RTX 3060 12GB for Local LLM Inference (2026)
• How Fast Is Local LLM Inference on a Ryzen 7 5800X (CPU-Only, No GPU)?

Citations and sources

• TechPowerUp — GeForce RTX 3060 GPU database — bandwidth, CUDA core, TGP reference
• llama.cpp discussions on quantization tradeoffs — q3 vs q4 community measurements
• Google AI for Developers — Gemma model documentation — base architecture, licensing, prompt template