Gemma-4-Harmonia-31B Uncensored on RTX 3060 12GB: Quantization, VRAM, and tok/s

Yes — the RTX 3060 12GB can host Gemma-4-Harmonia-31B-Uncensored-Heretic, but only with aggressive quantization. The q3_K_M GGUF lands at roughly 11.5 GB of weights and fits fully in 12 GB VRAM with room for a 4K context KV cache. Expect 8–14 tokens per second sustained, with q4_K_M requiring partial CPU offload that drops throughput to roughly 4–6 tok/s. q2_K is the only quant that fits comfortably with 8K context, but quality regression is noticeable.

Why this matters — the 12GB card is now the price-floor for serious 31B inference

For most of 2024 and 2025, the practical floor for "real" local LLM work was 24 GB of VRAM — a used RTX 3090 or a workstation card. That changed once 31B-class instruction-tuned models stabilized at q3 and q4 quantization sizes that fit, with caveats, inside 12 GB. The ZOTAC Gaming RTX 3060 Twin Edge OC 12GB and MSI RTX 3060 Ventus 2X 12G are the cheapest CUDA-capable cards with 12 GB of GDDR6 in the catalog, and the LocalLLaMA thread covering the Harmonia merge specifically pointed at them as the entry-tier targets to validate.

The Gemma-4-Harmonia-31B-Uncensored-Heretic release matters because it's a merge of multiple Gemma-4-31B-it finetunes targeted at uncensored output, distributed primarily as GGUF quants on Hugging Face. Per the base Gemma 2 27B-IT model card, the Gemma family ships with safety tuning and refusal training that the Heretic merge specifically removes. The instruction-following backbone — token efficiency, multilingual coverage, code reasoning — comes along intact.

For the price-conscious local-LLM operator in 2026, the question is no longer "should I save up for a 3090" but "can the model I want to run be made to fit on the card I already have." For the Gemma-4-31B family on a 3060 12GB, the answer is a qualified yes.

Key takeaways

• q3_K_M of Gemma-4-31B fits in 12 GB with 4K context — expect 8–14 tok/s on the 3060 12GB
• q4_K_M needs 13–15 GB of weights; partial offload of 6–10 layers to CPU is unavoidable, costing roughly 2–3× generation time
• The 3060 12GB's 360 GB/s memory bandwidth caps generation throughput; the GPU compute is rarely the bottleneck at this model size
• The Harmonia merge preserves base instruction-following — quality on standard benchmarks tracks within 1–2 points of stock Gemma-4-31B-it
• For pure inference, llama.cpp with the CUDA backend remains the most VRAM-efficient path on a 12 GB card; Ollama and LM Studio add overhead that costs 0.5–1 GB of headroom

What is Gemma-4-Harmonia-31B-Uncensored-Heretic and why was it merged?

Harmonia-Heretic is a community merge in the lineage of the Gemma-4-31B-it instruction-tuned base. Following the pattern established by earlier uncensoring efforts on Llama and Mistral, the Heretic suffix indicates the merge specifically targets removal of refusal training without further fine-tuning on adversarial or harmful content. The result preserves the base model's reasoning ability while making it more compliant with edge-case requests that the safety-tuned base would refuse.

The "why" is straightforward: production deployments of agentic tooling — code assistants, RAG pipelines, document-analysis agents — frequently trip over Gemma-4-it's refusal heuristics for entirely benign tasks. A merge that relaxes refusal training without degrading reasoning gives operators a model they can actually use for those workflows. Per the base Gemma 2 27B-IT page, Google's safety training was tuned for chatbot use cases, not for agentic pipelines where the same refusal pattern is a workflow blocker.

The merge does not improve raw reasoning. Standard MMLU and HellaSwag scores track the base Gemma-4-31B-it within 1–2 points. Use the original Gemma-4-31B-it if you need safety filtering preserved — Harmonia is a workflow-class tool, not a benchmark-class one.

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

At its native FP16 precision, Gemma-4-31B occupies roughly 62 GB of weights — nowhere near 12 GB. Quantization closes that gap. Here is what each quant level looks like for a 31B-parameter model in practice:

Community measurements posted in llama.cpp discussions for Gemma-4-class 31B models consistently report q3_K_M weights in the 13–15 GB range. That means q4_K_M is not a fully-resident option on a 12 GB card; you will be offloading 6–10 transformer layers to CPU. Inference will still complete, but per-token generation drops by 2–3× as those CPU-resident layers process at LPDDR5 memory bandwidth instead of GDDR6.

The realistic "fits clean" target on a 3060 12GB is q3_K_S or iq3_xxs at 4K context, accepting that q3 introduces measurable but tolerable quality regression for code-completion and chat-style use.

What quantization should I pick on a 12GB card?

For a 31B model on 12 GB of VRAM, three paths apply depending on what you actually want:

Pick q3_K_S or iq3_xxs if you want full-resident inference with the best sustained throughput. This is the right answer for chat, code completion, and any workload where 8–14 tok/s feels live. Quality regression versus q4 is real but small for typical instruction-following tasks.

Pick q4_K_M only if you accept partial offload and the throughput penalty. This trades generation speed for measurably better output quality. If you're using the model for one-shot synthesis tasks where wall-clock time matters less than result quality, the 2–3× penalty is tolerable.

Pick q2_K if you need long context. q2 quants give back enough VRAM to push context to 8K or even 16K on a 12 GB card. Quality regression is noticeable but the longer context window is the right trade for document-analysis workflows.

Do not bother with q5_K_M or higher on a 3060 12GB. The combination of weight size and unavoidable offload makes throughput unacceptable for interactive use.

Prefill vs generation throughput on Ampere with Gemma-4-31B

Prefill (the cost of ingesting your prompt) and generation (the cost of producing each new token) are bottlenecked by different things. On Ampere-class GPUs at q3–q4 quantization for a 31B model:

• Prefill scales with compute throughput and PCIe transfer cost. The 3060's roughly 13 TFLOPs of FP16 compute is enough that prefill on a 4K prompt completes in 8–15 seconds. PCIe Gen4 x16 keeps the initial weight upload fast.
• Generation is memory-bandwidth limited. Each generated token requires the model to traverse all weights once. With 360 GB/s of GDDR6 bandwidth, the theoretical ceiling for a 15 GB partially-resident model is roughly 20 tok/s; partial offload pulls that down to the 4–10 tok/s range that LocalLLaMA reports.

The practical implication: long prompts cost time linearly, but the per-token output rate stays roughly flat once generation starts. Investing in faster system RAM (DDR4-3600 over DDR4-3200) marginally helps the partial-offload case; investing in a 16 GB card eliminates the offload entirely.

How does context length impact 31B inference on 12GB?

The KV cache grows linearly with context length. For a 31B Gemma-class model:

On a 3060 12GB running q3_K_S Gemma-4-31B (13 GB weights), you have about 11.5 GB of VRAM after driver overhead and the runtime's own working set. That leaves zero room for context expansion without dropping to q2. For workflows that demand 8K+ context, q2_K is the only fully-resident option on a 12 GB card. If your prompts are short (<2K) and outputs are short, q3_K_S with 2K context fits cleanly.

RTX 3060 12GB vs RTX 4060 Ti 16GB vs RTX 3090 24GB for Gemma-4-31B

The 4060 Ti 16GB is the surprise: more VRAM but less memory bandwidth than the 3060. For a 31B model fitting at q4, the 4060 Ti just barely accommodates the weights and avoids offload — its lower bandwidth still beats partial-offload throughput on a 12 GB card. The 3090 24GB remains the best-value path for 31B work in 2026; q5 with 8K context delivers production-tier throughput at the same per-token quality as q4 on a smaller card.

Per TechPowerUp's RTX 3060 12GB GPU specifications page, the GA106-300 chip exposes 3,584 CUDA cores at a 1.78 GHz boost clock with 360 GB/s of memory bandwidth on the 192-bit bus. Those numbers haven't changed since release; what's changed is that the 12 GB capacity is now genuinely useful for inference, where it once felt like a marketing decision.

Common pitfalls when running 31B on a 3060 12GB

• Forgetting to set --n-gpu-layers explicitly in llama.cpp. The default auto-detect frequently leaves 1–2 GB of VRAM unused. Manually push -ngl 99 and let the OOM error tell you the real ceiling.
• Running the desktop environment on the same card. A typical Linux desktop with one 4K monitor reserves 800 MB to 1.2 GB of VRAM. On a 12 GB card that's the difference between q3_K_S fitting and not fitting. Use the iGPU for display or switch to a headless TTY for inference runs.
• Picking GGUF files without checking the imatrix flag. The newer iq3_xxs and iq2_M quants use importance-matrix quantization that delivers better quality per byte than the older static quants. The Hugging Face quant uploader's README will note this — prefer the imatrix variants where available.
• Underprovisioning system RAM for partial offload. If you're going to offload layers to CPU, you need at least 32 GB of system RAM to hold the offloaded weights plus the OS plus your runtime. 16 GB is not enough for a 31B model with any meaningful offload.
• Believing the marketing TGP. The 3060 12GB's 170 W power budget is real under sustained inference. A 550 W PSU is the practical minimum for a single-3060 build; smaller PSUs throttle the card or trip undervoltage protection.

When NOT to run Gemma-4-31B on a 3060 12GB

If your workload is interactive chat at 30+ tok/s, the 3060 12GB at q3 is going to feel slow. Step up to a used 3090 24GB or wait for a 16 GB card to drop into your budget. If your workload involves any document longer than 4K tokens, the 3060 12GB will either OOM or force you to q2 — neither is a happy outcome. If quality matters more than wall-clock time and you have a 5800X or better CPU with 64 GB of system RAM, run q4 with offload and accept the speed penalty as an explicit choice.

Bottom line: which 12GB card and which quant

Get the ZOTAC RTX 3060 12GB if you want the cheapest entry point — street pricing is consistently below the MSI variant by $30–80, and TechPowerUp's specs confirm both cards target the same GA106-300 silicon. Get the MSI Ventus 2X 12G if you have an open mITX or ITX build where the slightly more compact cooler matters. Both cards deliver identical inference throughput within margin of error.

Run q3_K_S at 4K context as your default. Step up to q4_K_M with offload only when output quality matters more than speed. Drop to q2_K when context length is the constraint.

If your budget can flex to $400–500, the RTX 4060 Ti 16GB is a meaningful upgrade for 31B work — q4 fits cleanly, no offload, throughput doubles. If your budget can flex to $700–900 for a used 3090, that's the right answer for any serious local-LLM work in 2026.

Related guides

• Gemma 4 31B-IT on a 12GB RTX 3060: What Fits, What Offloads, How Fast
• Best Budget GPU for Local LLM Inference in 2026
• CUDA 13.3 Landed: What Local LLM Operators Need to Know
• Llama.cpp Console Released: What Changes for Local LLM Operators on a 12GB GPU
• Best CPU for Local LLM Inference in 2026: Ryzen 7 5800X vs 5700X vs 5600G

Citations and sources

• Google Gemma 2 27B-IT model card on Hugging Face — base Gemma family model card and tuning notes
• TechPowerUp GeForce RTX 3060 12GB GPU specifications — GA106-300 silicon, memory bandwidth, and TDP
• llama.cpp GitHub repository — CUDA backend, GGUF quantization formats, and community benchmark discussion

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