# Gemma 3 12B Instruct on a GeForce RTX 4070 Ti SUPER

Yes, quantized. Q8_0 is the largest that fits, leaving room for 38k tokens of context.

Canonical page: https://makerportal.ai/lab/llm-vram/gemma-3-12b-it/rtx-4070-ti-super
Page title: Gemma 3 12B Instruct on RTX 4070 Ti SUPER — fits at Q8_0

This markdown document and the HTML page above are rendered from the same solved values at build time, by the same functions. Nothing here is written by a language model and nothing is fetched at request time.

## Key figures

- **Verdict:** Q8_0 — 14.7 GiB usable of 16 GB
- **Usable memory:** 14.7 GiB — 92% of the card's 16 GB — an assumption for a dedicated card, not a single fraction applied to every device
- **Weights at FP16:** 22.7 GiB — 12.2B parameters
- **Weights (Q4_K_M):** 6.9 GiB — 22.7 GiB at FP16 · 12.2B params · 1 of 6 quant rows are measured files
- **KV cache:** 384.0 KiB per token at FP16 — 48 layers × 8 KV heads × 256 dimensions
- **Speed ceiling:** 49 tok/s — 672 GB/s ÷ bytes read per token
- **Largest context that fits:** 38,465 tokens at Q8_0
- **Trained context:** 131,072 — the cap the KV-cache figures are held to
- **Card bandwidth:** 672 GB/s — 16 GB GDDR6X

## Every quant, against this card

Every quantization of Gemma 3 12B Instruct against the 14.7 GiB usable on a GeForce RTX 4070 Ti SUPER. "Measured" rows are the byte size of a real published file; "nominal" rows are the parameter count times the published bits-per-weight.

| Precision | Bits/weight | Weights | Source | Fits | Max context | Ceiling |
|---|---|---|---|---|---|---|
| FP16 / BF16 | 16.00 | 22.7 GiB | measured file | short by 8.0 GiB | — | 7.8 tok/s offloaded |
| Q8_0 | 8.50 | 12.1 GiB | nominal | yes | 38,465 | 49 tok/s |
| Q6_K | 6.56 | 9.3 GiB | nominal | yes | 83,562 | 62 tok/s |
| Q5_K_M | 5.67 | 8.0 GiB | nominal | yes | 104,250 | 71 tok/s |
| Q4_K_M | 4.83 | 6.9 GiB | nominal | yes | 123,776 | 82 tok/s |
| Q3_K_M | 3.91 | 5.5 GiB | nominal | yes | 131,072 (model cap) | 98 tok/s |

## Questions this page answers

### How much VRAM does Gemma 3 12B Instruct need?

22.7 GiB for the weights at FP16 — 12.2B parameters at two bytes each — and 6.9 GiB at Q4_K_M. The KV cache is on top of that and is not a fixed number: this model spends 384.0 KiB per token of context on its full-attention layers, so 8,192 tokens costs a further 832 MiB. A GeForce RTX 4070 Ti SUPER makes 14.7 GiB of its 16 GB available on the assumption below.

### Can a GeForce RTX 4070 Ti SUPER run Gemma 3 12B Instruct?

Yes, quantized. Q8_0 is the largest that fits, leaving room for 38k tokens of context. That is the weights and the KV cache together, against 14.7 GiB of usable memory.

### How fast will Gemma 3 12B Instruct run on a GeForce RTX 4070 Ti SUPER?

No faster than 49 tokens/second at Q8_0, and in practice below it. Decoding is memory-bound: every token reads all 12.1 GiB of weights plus the cache, and this card moves 672 GB/s. That division is the ceiling — no kernel, runtime or driver beats it, and a real runtime typically reaches 60–80% of it.

### Why does the context length change how much memory Gemma 3 12B Instruct needs?

Because the KV cache holds one key and one value vector per token, per layer, for the whole conversation, and it is allocated separately from the weights. This model has 48 layers and 8 key/value heads of 256 dimensions, shared across 16 query heads — grouped-query attention, which divides the cache by 2. That works out at 384.0 KiB per token, except that 40 of the 48 layers use a 1024-token sliding window and stop growing there. Parameter count tells you nothing about this number.

## Method and limits

Weight bytes are the byte size of the real published file wherever one exists, and the model's exact parameter count times the published llama.cpp bits-per-weight where it does not. That distinction is on every row above and it matters at both ends: a sub-1B model's Q4_K_M file runs a third larger than the nominal figure because k-quants keep its embedding tables at higher precision, and an already-4-bit release cannot be quantized upward at all. The KV cache is 2 · layers · kv_heads · head_dim · bytes per token, summed over layers with each sliding-window layer capped at its window. The speed figure is a roofline bound, not a benchmark: bandwidth divided by bytes read per token, which no runtime exceeds and every runtime falls short of. The usable fraction of card memory is an assumption: 92% here, which is what this lane assumes for a dedicated card — the other class assumes 75%, so it is not one number applied to every device. That is the only assumed input on this page; every other figure is computed from the model config and the card's published specification. Nothing on this page is written by a language model.

## Related tool

[LLM VRAM & KV-Cache Footprint Calculator](https://makerportal.ai/lab/llm-vram-kvcache-calculator) — Interactive VRAM planner — change the model geometry, quantization, context length and card capacity and read the weight bytes, KV-cache bytes and the bandwidth ceiling on decode speed.

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Source: MakerPortal — https://makerportal.ai/lab/llm-vram/gemma-3-12b-it/rtx-4070-ti-super. Free to quote and cite with attribution and a link to the canonical page.