Gemma 3 12B IT — Hardware Requirements & GPU Compatibility
Vision
Google Gemma 3 12B IT is a 12-billion parameter multimodal instruction-tuned model from Google's Gemma 3 series. It supports both text and image inputs, offering vision-language capabilities at a more accessible size point than the 27B variant. Gemma 3 12B IT runs on consumer GPUs with 12-16GB of VRAM in quantized formats, making it a practical choice for local multimodal inference without requiring top-tier hardware. Released under the Gemma license.
Runs great on a 24 GB GPU
Runs from 8 GB (IQ2_M · 16K)Best on 24 GB (Q4_K_M · ~82 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~17 tok/s)
Score 74.2#96 of 106 open#28 of 36 that fit 24 GBGemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
74.2
- 90% range 68.6–79.8
- #96 of 106 open models
- #28 of 36 that fit 24 GB
Run Gemma 3 12B IT
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run gemma3:12bQ4_K_M fits fully on the RTX 4090 · 13.1 GB free · 16K context
Download: gemma-3-12b-it-Q4_K_M.gguf7.30 GBunsloth
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3090 (24 GB) (13 GB headroom) · Q4_K_M
- Generation speed
- ~76 tok/s
- generation speed
- Cost per 1M output tokens
- $0.62
- per 1M output tokens
How Much VRAM Does Gemma 3 12B IT Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 5.7 GB | 11.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~115 tok/s | |
| 5.9 GB | 12.1 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~112 tok/s | |
| 6.5 GB | 12.7 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~100 tok/s | |
| 6.7 GB | 12.9 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~98 tok/s | |
| 8.0 GB | 14.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~82 tok/s | |
| 9.6 GB | 15.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~69 tok/s | |
| 11.1 GB | 17.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~59 tok/s | |
| 13.4 GB | 19.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~49 tok/s |
Selected Q4_K_M
4-bit medium quantization10.9GBat 16K context
- Weights
- 7.3 GB
- KV cache
- 3.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 13.1 GB free · ~82 tok/s
≥50 tok/s needs ≥618 GB/s · RTX 5090 ~145, RTX 3090 Ti ~82
Which Hardware Can Run Gemma 3 12B IT?
Q4_K_M · 8.0 GBGemma 3 12B IT (Q4_K_M) requires 8.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 11+ GB is recommended. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run Gemma 3 12B IT, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
12 GB
12 GB tier →fits · 1.1 GB free · up to 16K ctx
10 GPUs
RTX 3080 Ti~74Simulate output on RTX 3080 TiRTX 5070~54Simulate output on RTX 5070RTX 4070~41Simulate output on RTX 407020 GB
fits · 9.1 GB free · up to 32K ctx
1 GPU
RX 7900 XT~60Simulate output on RX 7900 XT32–40 GB
fits · 21.1 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~145Simulate output on RTX 5090M5 Max36 GB~40Simulate output on Mac Studio M5 Max (36 GB)M532 GB~13Simulate output on MacBook Air 13" M5 (32 GB)Orin~17Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~11Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Gemma 3 12B IT?
The llmrun Score merges 7 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the 24 GB tier
- Gemma 3 12B IT
- Other models
- Best Score for its VRAM
#28 of 36 that fit 24 GB
Each dot is a model at the best quant that fits a 24 GB GPU. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.
Full 24 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 8 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Mar 2025Independent
Independent39.5%
#73/100 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1341.7
#84/188 open
- AIME 2024/2025
Epoch AI · Mar 2025Independent
Independent16.7%
#57/88 open
- MMLU-Pro
TIGER-LabIndependent
Independent60.6%
#57/146 open
- SciCode
Epoch AI · Mar 2025Lab-reported
Lab-reported16.4%
#70/72 open
- CritPt
Epoch AI · Mar 2025Lab-reported
Lab-reported0.0%
#56/84 open
In the 24 GB class
36 models fit 24 GBSame Score, only models that fit 24 GB at their best quant: the ranking a 24 GB owner actually chooses from.
- #1
Qwen3.8 27BAlibaba
122.0
~38 tok/s · 17.9 GB
- #2
Qwen3.6 27BAlibaba
111.2
~38 tok/s · 17.9 GB
- #3
Qwen3.6 35B A3BAlibaba
106.8
~151 tok/s · 22.0 GB
- #4
Gemma 4 31B ITGoogle
106.7
~32 tok/s · 21.7 GB
- #5
Gemma 4 26B A4B ITGoogle
105.8
~171 tok/s · 16.5 GB
- #6
Qwen3.5 35B A3BAlibaba
105.8
~151 tok/s · 22.0 GB
Alternatives to Gemma 3 12B IT
Gemma 3 family · 20 models →Official models with an llmrun Score, each answering one question; numbers are at the best quant that fits a 24 GB GPU.
Best alternative at this size
GPT OSS 20B
OpenAI
- 22.2 higher Score
- 2.2 GB more VRAM
- 108 tok/s faster
Score 96.4 · 13.1 GB · ~190 tok/s
Much faster, similar quality
Qwen3.6 35B A3B
Alibaba
- 32.6 higher Score
- 11.1 GB more VRAM
- 70 tok/s faster
Score 106.8 · 22 GB · ~151 tok/s
Other vendor
Qwen3.8 27B
Alibaba
- 47.8 higher Score
- 6.9 GB more VRAM
- 44 tok/s slower
Score 122.0 · 17.9 GB · ~38 tok/s
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.6 27BAlibaba | 111.2 | 17.9 GB | ~38 | similar score |
| Gemma 4 31B ITGoogle | 106.7 | 21.7 GB | ~32 | similar score |
| Gemma 4 26B A4B ITGoogle | 105.8 | 16.5 GB | ~171 | similar score |
| Qwen3.5 27BAlibaba | 103.8 | 17.9 GB | ~38 | similar score |
| Qwen3 30B A3B Thinking 2507Alibaba | 100.7 | 19.4 GB | ~173 | similar score |
| Qwen3.5 9BAlibaba | 99.4 | 6.6 GB | ~103 | similar score |
| Magistral Small 2506Mistral AI | 88.7 | 17.8 GB | ~44 | similar score |
| Mistral Small 3.2 24B Instruct 2506Mistral AI | 85.7 | 18.1 GB | ~43 | similar score |
| Magistral Small 2509Mistral AI | 85.6 | 18.1 GB | ~43 | similar score |
| Mistral Small 3.1 24B Instruct 2503Mistral AI | 79.0 | 18.1 GB | ~43 | similar score |
Specifications
- Publisher
- Family
- Gemma 3
- Parameters
- 12.2B
- Context Length
- 32,768 tokens
- Release Date
- 2025-03-01
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does Gemma 3 12B IT need?
Gemma 3 12B IT requires 8.0 GB of VRAM at Q4_K_M, or 26.8 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 12.2B × 4.8 bits ÷ 8 = 7.3 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Gemma 3 12B IT?
Yes, at Q8_0 (13.4 GB) or lower. Higher quantizations like BF16 (26.8 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Gemma 3 12B IT?
For Gemma 3 12B IT, Q4_K_M (8.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (8.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 3.7 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma 3 12B IT on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Gemma 3 12B IT. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 8.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 3 12B IT locally?
Yes — Gemma 3 12B IT can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 3 12B IT?
At Q4_K_M, Gemma 3 12B IT can reach ~597 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~82 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 8.04 × 0.65 = ~647 tok/s
Estimated speed at Q4_K_M (8.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gemma 3 12B IT?
At Q4_K_M, the download is about 7.31 GB. The full-precision BF16 version is 24.37 GB. The smallest option (IQ2_XXS) is 3.35 GB.
Which GPUs can run Gemma 3 12B IT?
40 consumer GPUs can run Gemma 3 12B IT at Q4_K_M. Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
Which devices can run Gemma 3 12B IT?
150 devices with unified memory can run Gemma 3 12B IT at Q4_K_M (8.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.