Gemma · 9B to 32B
Gemma 4 12B Instruct
11.96 billion parameters across 48 layers. At four-bit precision the weights alone come to 6.72 GB, before any conversation is loaded. This one is cache-hungry: a 32k window adds 4.3 GB on top, so context length matters more here than the model size suggests.
About Gemma 4 12B Instruct
Gemma 4 12B Instruct is a dense model with 11.96B parameters, built from 48 layers with a hidden size of 3840. Its repository was published on 2026-05-23. The license listed on Hugging Face is apache-2.0. 40 of its 48 layers use a sliding window of 1k tokens, so only 8 keep growing with the conversation.
Memory needed, by quantization
At an 8k context window. Quality is our rough ranking of how much the compression costs you: anything at or above Q5 is hard to tell apart from the original in normal use.
| Quantization | File size | KV cache | Total VRAM | Quality | What it costs you |
|---|---|---|---|---|---|
| Q8_0 | 11.83 GB | 1.31 GB | 13.55 GB | 99% | Lossless in practice. Use it when the memory is there. |
| Q6_K | 9.13 GB | 1.31 GB | 10.84 GB | 98% | Very close to Q8 for two thirds of the size. |
| Q5_K_M | 7.92 GB | 1.31 GB | 9.63 GB | 96% | The quality-first choice when Q6 will not fit. |
| Q4_K_M | 6.72 GB | 1.31 GB | 8.44 GB | 93% | The default. Best size-to-quality ratio for local use. |
| IQ4_XS | 5.92 GB | 1.31 GB | 7.63 GB | 91% | Importance-matrix 4-bit. Q4_K_S quality, smaller file. |
| Q3_K_M | 5.44 GB | 1.31 GB | 7.16 GB | 86% | Degradation starts to show. A way to fit one size up. |
| IQ3_XXS | 4.26 GB | 1.31 GB | 5.97 GB | 79% | Aggressive. Only worth it on very large models. |
What a longer conversation costs
Same model at Q4_K_M, only the context window changes. 40 of its 48 layers use sliding-window attention, so their cache stops growing past 1k tokens and only the other 8 keep growing.
| Context | KV cache | Total VRAM | Fits in 8 GB | Fits in 12 GB | Fits in 24 GB |
|---|---|---|---|---|---|
| 4k | 0.81 GB | 7.94 GB | no | yes | yes |
| 8k | 1.31 GB | 8.44 GB | no | yes | yes |
| 16k | 2.31 GB | 9.44 GB | no | yes | yes |
| 32k | 4.31 GB | 11.44 GB | no | no | yes |
| 64k | 8.31 GB | 15.44 GB | no | no | yes |
| 128k | 16.31 GB | 23.44 GB | no | no | no |
The "fits" columns allow for the roughly 0.8 GB Windows keeps for the desktop.
Which hardware runs Gemma 4 12B Instruct
107 of 118 consumer devices run it at a quantization worth using, at an 8k context window. Another 2 can load it only by compressing the weights far enough to damage the model, marked with a warning below.
| Device | Memory | Verdict | Quantization | Used | Speed |
|---|---|---|---|---|---|
| GeForce RTX 5090 NVIDIA | 32 GB | Runs great | Q8_0 | 13.55 GB | 100.3 t/s |
| RTX PRO 6000 Blackwell NVIDIA | 96 GB | Runs great | Q8_0 | 13.55 GB | 100.3 t/s |
| GeForce RTX 4090 NVIDIA | 24 GB | Runs great | Q8_0 | 13.55 GB | 56.4 t/s |
| GeForce RTX 3090 Ti NVIDIA | 24 GB | Runs great | Q8_0 | 13.55 GB | 56.4 t/s |
| GeForce RTX 5080 NVIDIA | 16 GB | Runs great | Q8_0 | 13.55 GB | 53.7 t/s |
| RTX 6000 Ada Generation NVIDIA | 48 GB | Runs great | Q8_0 | 13.55 GB | 53.7 t/s |
| GeForce RTX 3090 NVIDIA | 24 GB | Runs great | Q8_0 | 13.55 GB | 52.4 t/s |
| GeForce RTX 5070 Ti NVIDIA | 16 GB | Runs great | Q8_0 | 13.55 GB | 50.1 t/s |
| GeForce RTX 5090 Laptop NVIDIA | 24 GB | Runs great | Q8_0 | 13.55 GB | 50.1 t/s |
| Radeon RX 7900 XTX AMD | 24 GB | Runs great | Q8_0 | 13.55 GB | 45.6 t/s |
| GeForce RTX 5080 Laptop NVIDIA | 16 GB | Runs great | Q8_0 | 13.55 GB | 43 t/s |
| RTX A6000 NVIDIA | 48 GB | Runs great | Q8_0 | 13.55 GB | 43 t/s |
| RTX A5000 NVIDIA | 24 GB | Runs great | Q8_0 | 13.55 GB | 43 t/s |
| Apple M3 Ultra 96GB Apple | 96 GB | Runs great | Q8_0 | 13.52 GB | 42.4 t/s |
| Apple M3 Ultra 256GB Apple | 256 GB | Runs great | Q8_0 | 13.52 GB | 42.4 t/s |
| Apple M3 Ultra 512GB Apple | 512 GB | Runs great | Q8_0 | 13.52 GB | 42.4 t/s |
| Apple M1 Ultra 64GB Apple | 64 GB | Runs great | Q8_0 | 13.52 GB | 41.4 t/s |
| Apple M1 Ultra 128GB Apple | 128 GB | Runs great | Q8_0 | 13.52 GB | 41.4 t/s |
| Apple M2 Ultra 64GB Apple | 64 GB | Runs great | Q8_0 | 13.52 GB | 41.4 t/s |
| Apple M2 Ultra 128GB Apple | 128 GB | Runs great | Q8_0 | 13.52 GB | 41.4 t/s |
| Apple M2 Ultra 192GB Apple | 192 GB | Runs great | Q8_0 | 13.52 GB | 41.4 t/s |
| GeForce RTX 4080 SUPER NVIDIA | 16 GB | Runs great | Q8_0 | 13.55 GB | 41.2 t/s |
| Radeon PRO W7900 AMD | 48 GB | Runs great | Q8_0 | 13.55 GB | 41 t/s |
| GeForce RTX 4080 NVIDIA | 16 GB | Runs great | Q8_0 | 13.55 GB | 40.1 t/s |
| Radeon RX 7900 XT AMD | 20 GB | Runs great | Q8_0 | 13.55 GB | 38 t/s |
Show 35 more devices
| GeForce RTX 4070 Ti SUPER NVIDIA | 16 GB | Runs great | Q8_0 | 13.55 GB | 37.6 t/s |
| GeForce RTX 4090 Laptop NVIDIA | 16 GB | Runs great | Q8_0 | 13.55 GB | 32.2 t/s |
| Radeon RX 9070 XT AMD | 16 GB | Runs great | Q8_0 | 13.55 GB | 30.6 t/s |
| Radeon RX 9070 AMD | 16 GB | Runs great | Q8_0 | 13.55 GB | 30.6 t/s |
| Radeon RX 7800 XT AMD | 16 GB | Runs great | Q8_0 | 13.55 GB | 29.6 t/s |
| Apple M4 Max 36GB Apple | 36 GB | Runs great | Q8_0 | 13.52 GB | 28.2 t/s |
| Apple M4 Max 48GB Apple | 48 GB | Runs great | Q8_0 | 13.52 GB | 28.2 t/s |
| Apple M4 Max 64GB Apple | 64 GB | Runs great | Q8_0 | 13.52 GB | 28.2 t/s |
| Apple M4 Max 128GB Apple | 128 GB | Runs great | Q8_0 | 13.52 GB | 28.2 t/s |
| Radeon RX 7900 GRE AMD | 16 GB | Runs great | Q8_0 | 13.55 GB | 27.3 t/s |
| GeForce RTX 5060 Ti 16GB NVIDIA | 16 GB | Runs great | Q8_0 | 13.55 GB | 25.1 t/s |
| RTX A4000 NVIDIA | 16 GB | Runs great | Q8_0 | 13.55 GB | 25.1 t/s |
| Radeon RX 6900 XT AMD | 16 GB | Runs well | Q8_0 | 13.55 GB | 24.3 t/s |
| Radeon RX 6800 AMD | 16 GB | Runs well | Q8_0 | 13.55 GB | 24.3 t/s |
| Arc A770 16GB Intel | 16 GB | Runs well | Q8_0 | 13.55 GB | 24.2 t/s |
| Apple M1 Max 32GB Apple | 32 GB | Runs well | Q8_0 | 13.52 GB | 20.7 t/s |
| Apple M1 Max 64GB Apple | 64 GB | Runs well | Q8_0 | 13.52 GB | 20.7 t/s |
| Apple M2 Max 32GB Apple | 32 GB | Runs well | Q8_0 | 13.52 GB | 20.7 t/s |
| Apple M2 Max 64GB Apple | 64 GB | Runs well | Q8_0 | 13.52 GB | 20.7 t/s |
| Apple M2 Max 96GB Apple | 96 GB | Runs well | Q8_0 | 13.52 GB | 20.7 t/s |
| Apple M3 Max 36GB Apple | 36 GB | Runs well | Q8_0 | 13.52 GB | 20.7 t/s |
| Apple M3 Max 48GB Apple | 48 GB | Runs well | Q8_0 | 13.52 GB | 20.7 t/s |
| Apple M3 Max 64GB Apple | 64 GB | Runs well | Q8_0 | 13.52 GB | 20.7 t/s |
| Apple M3 Max 96GB Apple | 96 GB | Runs well | Q8_0 | 13.52 GB | 20.7 t/s |
| Apple M3 Max 128GB Apple | 128 GB | Runs well | Q8_0 | 13.52 GB | 20.7 t/s |
| GeForce RTX 3080 Ti NVIDIA | 12 GB | Just fits | Q6_K | 10.84 GB | 64.2 t/s |
| GeForce RTX 3080 12GB NVIDIA | 12 GB | Just fits | Q6_K | 10.84 GB | 64.2 t/s |
| GeForce RTX 5070 NVIDIA | 12 GB | Just fits | Q6_K | 10.84 GB | 47.3 t/s |
| GeForce RTX 4070 Ti NVIDIA | 12 GB | Just fits | Q6_K | 10.84 GB | 35.5 t/s |
| GeForce RTX 4070 SUPER NVIDIA | 12 GB | Just fits | Q6_K | 10.84 GB | 35.5 t/s |
| GeForce RTX 4070 NVIDIA | 12 GB | Just fits | Q6_K | 10.84 GB | 35.5 t/s |
| GeForce RTX 4080 Laptop NVIDIA | 12 GB | Just fits | Q6_K | 10.84 GB | 30.4 t/s |
| Radeon RX 7700 XT AMD | 12 GB | Just fits | Q6_K | 10.84 GB | 25.8 t/s |
| GeForce RTX 3060 12GB NVIDIA | 12 GB | Just fits | Q6_K | 10.84 GB | 25.4 t/s |
| Arc B580 Intel | 12 GB | Just fits | Q6_K | 10.84 GB | 24.8 t/s |
Can my GPU run Gemma 4 12B Instruct?
A direct answer for popular devices, each with the settings we would use.
How it compares
Other models in the same family or of a similar size, on a 12 GB card and a 24 GB card, each at the best quantization that fits.
| Model | Weights at 4 bits | GeForce RTX 3060 12GB | GeForce RTX 4090 |
|---|---|---|---|
| Gemma 4 12B Instruct | 6.7 GB | 25.4 t/s at Q6_K | 56.4 t/s at Q8_0 |
| Gemma 3 12B Instruct | 6.9 GB | 26.2 t/s at Q6_K | 57.6 t/s at Q8_0 |
| Gemma 2 9B Instruct | 5.2 GB | 29.3 t/s at Q6_K | 66.7 t/s at Q8_0 |
| Gemma 4 E4B Instruct | 4.5 GB | 32.9 t/s at Q8_0 | 92 t/s at Q8_0 |
| Qwen3 8B | 4.6 GB | 30.8 t/s at Q8_0 | 86.1 t/s at Q8_0 |
| Qwen3.5 9B | 5.4 GB | 30 t/s at Q8_0 | 83.9 t/s at Q8_0 |
What to buy to run Gemma 4 12B Instruct
The cheapest hardware that runs it at a quantization worth using and a speed you would not resent, at an 8k context window.
Cheapest that works
GeForce RTX 2060 12GB
$160 used
Q6_K · uses 10.84 GB of its 12 GB · about 23.7 tokens/s
Best value
GeForce RTX 3060 12GB
$200 used
Q6_K · about 25.4 tokens/s · 360 GB/s
| Device | Price | Memory | Runs it at | Speed | |
|---|---|---|---|---|---|
| GeForce GTX 1080 Ti NVIDIA | $150 used | 11 GB | Q5_K_M | 38.6 t/s | Check price |
| GeForce RTX 2060 12GB NVIDIA | $160 used | 12 GB | Q6_K | 23.7 t/s | Check price |
| GeForce RTX 2080 Ti NVIDIA | $250 used | 11 GB | Q5_K_M | 49.1 t/s | Check price |
| Arc A770 16GB Intel | $280 used | 16 GB | Q8_0 | 24.2 t/s | Check price |
| GeForce RTX 3080 10GB NVIDIA | $350 used | 10 GB | Q4_K_M | 69.6 t/s | Check price |
| Radeon RX 7900 XT AMD | $650 | 20 GB | Q8_0 | 38 t/s | Check price |
| GeForce RTX 3090 NVIDIA | $700 used | 24 GB | Q8_0 | 52.4 t/s | Check price |
| Ryzen AI Max+ 395 32GB AMD · whole machine | $1,299 | 32 GB | Q5_K_M | 15.7 t/s | Check price |
Indicative prices reviewed 2026-09-01; used prices are marketplace typical. Options that cost more than a cheaper one with no more memory and no more speed are hidden. Price links are Amazon affiliate links. Change the standard, the context window or the budget in the buying tool.
Where it sits in the catalog
Source: google/gemma-4-12B-it on Hugging Face. Downloaded 3.3M times in the last month. Published 2026-05-23. Architecture figures are read from the repository's own configuration file, so they move when the model does.
Gemma 3 12B Instruct
12.19B · 128k context
Gemma 2 9B Instruct
9.24B · 8k context
Gemma 4 E4B Instruct
8B · 128k context
Gemma 4 E2B Instruct
5.12B · 128k context
Gemma 3 4B Instruct
4.3B · 128k context
Gemma 2 2B Instruct
2.61B · 8k context
Gemma 3 1B Instruct
1B · 32k context
Gemma 4 26B A4B Instruct
25.81B (3.74B active) · 256k context
Questions people ask about Gemma 4 12B Instruct
How much VRAM does Gemma 4 12B Instruct need?
At Q4_K_M with an 8k context window, Gemma 4 12B Instruct needs about 8.44 GB: 6.72 GB of weights, 1.31 GB of KV cache and 0.4 GB of overhead. At the highest-quality file it needs about 13.55 GB.
What is the cheapest GPU that runs Gemma 4 12B Instruct?
The smallest consumer device in our catalog that fits it is the GeForce RTX 3070 Ti with 8 GB, running it at Q3_K_M at roughly 66.2 tokens per second.
How much does a longer context window add for Gemma 4 12B Instruct?
On the memory side, a 32k window adds 4.3 GB of KV cache on top of the weights, and the cache grows more slowly than a conventional model because of its attention design. The longest window the model supports natively is 256k tokens.