Gemma 3 VRAM Requirements — 1B, 4B, 12B, 27B GPU & Mac Guide (2026)
Exact VRAM for Gemma 3 1B, 4B, 12B, and 27B at Q4, Q8, and FP16. Gemma 3 12B needs ~6.7GB at Q4, 27B needs ~15.1GB. Full GPU and Mac recommendations for 2026.
If you are searching for Gemma 3 12B or Gemma 3 27B VRAM requirements, this guide gives exact quantization tables, hardware recommendations, and step-by-step setup instructions.
If you only care about the most practical single model in the family, use the focused landing page here: Gemma 3 12B VRAM Requirements.
Gemma 3 is Google's most capable open-weight model family. Released in early 2026, it delivers strong general-purpose performance across a remarkably small VRAM footprint - making it one of the best choices for local inference at every size tier.
This guide covers all four Gemma 3 variants with exact VRAM requirements, hardware recommendations, and step-by-step setup instructions.
Gemma 3 at a Glance
- Gemma 3 1B: ~0.6 GB at Q4_K_M
- Gemma 3 4B: ~2.2 GB at Q4_K_M
- Gemma 3 12B: ~6.7 GB at Q4_K_M
- Gemma 3 27B: ~15.1 GB at Q4_K_M
The Gemma 3 Model Family
Google designed Gemma 3 specifically with local deployment in mind. The family spans four sizes:
- Gemma 3 1B — ultra-lightweight, fits on edge devices and integrated graphics
- Gemma 3 4B — efficient small model, fast on any modern GPU
- Gemma 3 12B — the sweet spot, strong quality at modest VRAM cost
- Gemma 3 27B — high-end quality, fits on a single RTX 4090
All variants use a dense transformer architecture (no Mixture of Experts), which means simpler hardware requirements and more predictable memory usage. Gemma 3 also supports a 128K context window — a notable advantage for long document tasks.
VRAM Requirements by Variant
Here's what each Gemma 3 variant needs at different quantization levels:
| Variant | Q4_K_M | Q5_K_M | Q6_K | Q8_0 | F16 |
|---|---|---|---|---|---|
| Gemma 3 1B | 0.6 GB | 0.8 GB | 0.9 GB | 1.1 GB | 2.0 GB |
| Gemma 3 4B | 2.2 GB | 2.7 GB | 3.2 GB | 4.2 GB | 8.0 GB |
| Gemma 3 12B | 6.7 GB | 8.2 GB | 9.7 GB | 12.6 GB | 24.0 GB |
| Gemma 3 27B | 15.1 GB | 18.6 GB | 19.4 GB | 25.4 GB | 54.0 GB |
Note: These are model weight sizes. Add ~1-2 GB for KV cache and runtime overhead at default context lengths. For long contexts (32K+), KV cache can add several GB.
Hardware Recommendations
Gemma 3 1B — Runs Anywhere
The 1B variant is built for speed and accessibility. It loads into less than 1GB of VRAM at Q4 and runs on virtually any hardware made in the last decade — including integrated graphics, older GPUs, and low-memory devices.
Recommended hardware:
- Any GPU with 2GB+ VRAM
- CPU-only inference (fast enough for casual use)
- RTX 4060 8GB — runs at F16 with room to spare
- Any Mac with 8GB+ unified memory
Quick start:
ollama run gemma3:1b
Best for: Edge deployment, offline chatbots, fast prototyping, devices with no dedicated GPU.
Gemma 3 4B — Efficient and Fast
The 4B is an excellent daily driver. It fits at full Q8 quality on an 8GB GPU and delivers surprisingly strong output for its size. If you want a snappy, no-fuss local model, this is it.
Recommended hardware:
- RTX 4060 8GB — runs at Q8 (~4.2 GB), fast inference
- RTX 4070 12GB — comfortable at Q8, plenty of context room
- Mac M3 16GB — runs smoothly at Q8
- Any GPU with 6GB+ VRAM handles Q5_K_M with ease
Quick start:
ollama run gemma3:4b
Check compatibility: Gemma 3 4B on RTX 4060 | Gemma 3 4B on RTX 4070
Best for: General chat, summarization, quick coding assistance, users with entry-level to mid-range GPUs.
Gemma 3 12B — The Sweet Spot
This is the variant most people should run. At Q4_K_M it needs only 6.7GB, meaning it fits on an 8GB GPU while delivering quality that rivals much larger models from previous generations. The 12B is where Gemma 3's efficiency gains really shine.
Recommended hardware:
- RTX 4060 8GB — fits at Q4 with 1+ GB headroom
- RTX 4070 12GB — fits at Q6 (~9.7GB), excellent quality/speed
- RTX 4070 Ti Super 16GB — runs Q8 comfortably
- Mac M4 Pro 24GB — excellent throughput at Q6+
Quick start:
ollama run gemma3:12b
Check compatibility: Gemma 3 12B on RTX 4070 | Gemma 3 12B on RTX 4060
Best for: The best overall Gemma 3 experience for mainstream hardware. Strong at writing, analysis, coding, and multilingual tasks.
Gemma 3 27B — High-End Single GPU
The 27B is Google's most capable consumer-friendly variant. It fits on a single RTX 4090 at Q4_K_M with 9GB to spare for context and KV cache — and at Q6_K it still fits with adequate headroom. For users with 24GB cards, this is the top-tier option without requiring multi-GPU setups.
Recommended hardware:
- RTX 4090 24GB — fits at Q4 (~15.1GB), comfortable at Q6 (~19.4GB)
- RTX 5090 32GB — fits at Q8 (~25.4GB), best consumer performance
- Mac M4 Max 36GB — comfortable at Q6+
- Mac M4 Max 64GB — runs at near-full precision easily
Quick start:
ollama run gemma3:27b
Check compatibility: Gemma 3 27B on RTX 4090
Best for: Complex reasoning, long-form writing, advanced coding tasks, users wanting frontier-adjacent quality on a single consumer GPU.
Choosing the Right Quantization
Gemma 3 handles quantization well across the board — it's a general-purpose model rather than a specialized reasoning model, so lower quantization has less impact than it would on something like DeepSeek R1.
Recommended quantization by VRAM:
| Available VRAM | Gemma 3 12B | Gemma 3 27B |
|---|---|---|
| 8 GB | Q4_K_M (6.7 GB) | — |
| 12 GB | Q6_K (9.7 GB) | — |
| 16 GB | Q8_0 (12.6 GB) | Q4_K_M (15.1 GB) |
| 24 GB | F16 (24.0 GB) | Q6_K (19.4 GB) |
| 32 GB+ | F16 | Q8_0 (25.4 GB) |
General guidance:
- Q6_K — best balance of quality and size; recommended when it fits
- Q5_K_M — minimal quality difference from Q6, saves ~15% VRAM
- Q4_K_M — good for fitting into tight VRAM budgets; output quality remains strong
- Avoid Q3 and below unless you're severely memory-constrained
For a deeper explanation of quantization formats, see our GGUF quantization guide.
Gemma 3 vs Competitors at Each Size
How does Gemma 3 stack up against the alternatives?
1B–4B Tier
| Model | VRAM (Q4) | Speed | Strengths |
|---|---|---|---|
| Gemma 3 1B | 0.6 GB | Very fast | Efficiency, edge deployment |
| Gemma 3 4B | 2.2 GB | Fast | Quality per GB, multilingual |
| Llama 3.2 3B | 1.9 GB | Fast | Strong general purpose |
| Phi-4 Mini 3.8B | 2.3 GB | Fast | Reasoning, coding focus |
Gemma 3 4B competes well with Phi-4 Mini while using less VRAM. It also has stronger multilingual support than most 4B alternatives.
12B Tier
| Model | VRAM (Q4) | Benchmark Score | Strengths |
|---|---|---|---|
| Gemma 3 12B | 6.7 GB | Strong | Efficiency, long context |
| Llama 3.1 8B | 4.5 GB | Good | Speed, wide ecosystem |
| Mistral Small 22B | 12.5 GB | Very good | Reasoning, code |
| Qwen2.5 14B | 7.8 GB | Very good | Math, coding |
Gemma 3 12B at 6.7GB Q4 fits on hardware where Mistral Small 22B and Qwen 14B don't. It punches well above its VRAM class.
27B Tier
| Model | VRAM (Q4) | Benchmark Score | Strengths |
|---|---|---|---|
| Gemma 3 27B | 15.1 GB | Excellent | Quality, long context |
| Mistral Small 22B | 12.5 GB | Very good | Smaller footprint |
| Qwen2.5 32B | 18.4 GB | Excellent | Math and coding |
| Llama 3.3 70B | 39.5 GB | Excellent | Best open general model |
Gemma 3 27B fits on a 16GB GPU at Q4, where Llama 3.3 70B needs 40GB. If you have 24GB, running Q6 gives near-full quality. It's the most powerful model that fits comfortably on a single RTX 4090 without quantization compromises.
Performance Expectations
Decode speed is primarily determined by your GPU's memory bandwidth:
| Hardware | Gemma 3 4B | Gemma 3 12B | Gemma 3 27B |
|---|---|---|---|
| RTX 4060 8GB | ~90 tok/s | ~40 tok/s | — |
| RTX 4070 12GB | ~100 tok/s | ~55 tok/s | — |
| RTX 4090 24GB | ~130 tok/s | ~80 tok/s | ~35 tok/s |
| RTX 5090 32GB | ~160 tok/s | ~100 tok/s | ~50 tok/s |
| Mac M4 Pro 24GB | ~55 tok/s | ~30 tok/s | ~15 tok/s |
| Mac M4 Max 64GB | ~65 tok/s | ~40 tok/s | ~20 tok/s |
Approximate values with Q4_K_M quantization via llama.cpp or Ollama. Actual performance varies by runtime, batch size, and context length.
Quick-Start Setup
Option 1: Ollama (Easiest)
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Run Gemma 3 12B (recommended starting point)
ollama run gemma3:12b
# Or choose another size
ollama run gemma3:1b # lightweight
ollama run gemma3:4b # fast and efficient
ollama run gemma3:27b # best quality
Option 2: LM Studio (GUI)
- Download LM Studio
- Search for "Gemma 3" in the model browser
- Download your preferred variant and quantization
- Load and start chatting
LM Studio shows estimated VRAM usage before you download, making it easy to pick the right quantization for your hardware.
Option 3: llama.cpp (Advanced)
# Download a GGUF file from Hugging Face
# Example: Gemma 3 12B Q4_K_M
wget https://huggingface.co/bartowski/gemma-3-12b-it-GGUF/resolve/main/gemma-3-12b-it-Q4_K_M.gguf
# Run with llama.cpp
./llama-cli -m gemma-3-12b-it-Q4_K_M.gguf \
-ngl 99 \ # GPU layers (99 = all)
-c 8192 \ # context size
-p "Your prompt here"
Option 4: vLLM (Production / API Server)
pip install vllm
# Serve Gemma 3 12B as an OpenAI-compatible API
vllm serve google/gemma-3-12b-it \
--max-model-len 32768 \
--gpu-memory-utilization 0.85
Gemma 3 for Specific Use Cases
Coding
Gemma 3 handles code well, especially the 12B and 27B variants. For dedicated coding use:
- 12B is good for code completion, explanation, and debugging in most languages
- 27B handles more complex tasks: architecture design, multi-file reasoning, harder algorithmic problems
For pure coding tasks, also consider Qwen2.5-Coder 32B or DeepSeek Coder V2 which are purpose-built for code.
Long Documents
Gemma 3's 128K context window is a standout feature. Most local models top out at 8K-32K. If you need to process long documents, research papers, or large codebases in a single context, Gemma 3 is one of the best local options.
Context memory usage note: A 32K context with the 12B model adds roughly 2-4GB of KV cache on top of the model weights. Plan accordingly when working with long contexts.
Multilingual Use
Gemma 3 was trained on a more diverse multilingual corpus than most comparably-sized open models. It handles European languages, East Asian languages, and Arabic noticeably better than Llama 3.1 at the same size tier.
Chat and Instruction Following
All Gemma 3 variants use instruction-tuned checkpoints optimized for chat (the -it suffix on HuggingFace). They follow instructions reliably and are well-suited as general-purpose assistants.
Checking Your Hardware Compatibility
Not sure which Gemma 3 variant fits your setup? Use our VRAM calculator to get an instant answer for your specific GPU or Mac.
You can also check specific combinations directly:
Or browse by hardware to see all compatible models:
Summary
Gemma 3 is one of the most VRAM-efficient model families available today. The key takeaways:
- 1B: Runs on anything, including CPU-only. Good for edge use cases.
- 4B: Best-in-class at its size. Fits at Q8 on 8GB VRAM. Great daily driver.
- 12B: The strongest recommendation for most users. 6.7GB at Q4 on an 8GB GPU is remarkable value.
- 27B: Fits on a single RTX 4090 at Q6. Near-frontier quality on consumer hardware.
For a broader look at what your GPU can handle, see our GPU recommendations guide or compare Gemma against other model families in our VRAM requirements reference.