Google DeepMindGeneralLicense: Apache 2.0
Gemma 4 12B
A well-rounded multimodal model for 16GB machines.
Gemma 4 12B (added in 2026) is a dense multimodal model that fits comfortably on 16GB Macs and 12-16GB GPUs.
Parameter Count12B
Context Window256k tokens
Min VRAM (Q4)9 GB
Recommended RAM12 GB+
⚡ Key Architectural Strengths
✓Multimodal input
✓Fits 16GB machines
✓Apache 2.0 licence
VRAM & Quantization Memory Footprint
Exact file weights, minimum VRAM to boot, and recommended headroom for extended context windows.
| Quantization Tier | Weight Size | Min VRAM/RAM | Recommended VRAM | Recommended Use Case |
|---|---|---|---|---|
| Q4_K_M (Recommended) | 7.2 GB | 8.7 GB | 10.5 GB | 4-bit weights. The usual size/quality sweet spot for local use. |
| Q8_0 (High Precision) | 12.7 GB | 14.7 GB | 17.1 GB | 8-bit weights. Close to full quality at about half the size of BF16. |
| BF16 / FP16 (Unquantized) | 24 GB | 27 GB | 30.6 GB | Unquantized weights, mainly for fine-tuning or GPU servers. |
⚡ Need to calculate exact KV Cache with 16k or 32k context?
Simulate exact VRAM allocations across various context lengths and precision depths.
1-Click Local Run Commands
Copy and paste directly into your terminal or local server engine.
Ollama CLI:
ollama run gemma4:12bLM Studio CLI:
lms get gemma-4-12bvLLM Production Server:
vllm serve google/gemma-4-12B-itllama.cpp Binary:
./llama-server -m gemma-4-12B-it-Q4_K_M.gguf -c 16384Hardware Compatibility & Speed Estimates
Estimated against 55+ Apple Silicon, Qualcomm, and NVIDIA hardware profiles.
| Device / Hardware Name | Platform | RAM/VRAM | Fit Status | Est. Tokens/Sec | Notes |
|---|---|---|---|---|---|
| Apple MacBook Air 13-inch (M4, 2025) | macOS | 16 GB | Comfortable | 10.8 t/s | Fits fully in GPU-usable memory (~10.7GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| HP Omnibook 5 OLED | Windows ARM | 32 GB | Comfortable | 12.2 t/s | Fits fully in GPU-usable memory (~19.2GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple MacBook Pro 16-inch (M4 Max) | macOS | 64 GB | Comfortable | 49.3 t/s | Fits fully in GPU-usable memory (~48GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple Mac mini (M4, 16GB) | macOS | 16 GB | Comfortable | 10.8 t/s | Fits fully in GPU-usable memory (~10.7GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple Mac mini (M4 Pro, 24GB) | macOS | 24 GB | Comfortable | 24.6 t/s | Fits fully in GPU-usable memory (~16.1GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple Mac Studio (M2 Ultra 192GB) | macOS | 192 GB | Comfortable | 72.2 t/s | Fits fully in GPU-usable memory (~144GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Microsoft Surface Laptop 7 | Windows ARM | 16 GB | Tight Fit | 11 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| Lenovo ThinkPad T14s Gen 5 (Snapdragon) | Windows ARM | 32 GB | Comfortable | 12.2 t/s | Fits fully in GPU-usable memory (~19.2GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| ASUS Zenbook S 16 (Ryzen AI 9 HX 370) | Windows x86 | 32 GB | Comfortable | 10.8 t/s | Fits fully in GPU-usable memory (~19.2GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Dell XPS 13 (Intel Core Ultra 7 258V) | Windows x86 | 32 GB | Comfortable | 12.3 t/s | Fits fully in GPU-usable memory (~19.2GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| ASUS ROG Zephyrus G16 (RTX 4070 Laptop) | Windows x86 | 32 GB | CPU Offload | 8.1 t/s | Exceeds the 8GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Razer Blade 16 (RTX 4090 Mobile 16GB) | Windows x86 | 32 GB | Comfortable | 52 t/s | Fits fully in VRAM (~16GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Framework Laptop 13 (Ryzen 7 7840U) | Linux | 64 GB | Comfortable | 8.1 t/s | Fits fully in GPU-usable memory (~38.4GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Lenovo Legion Pro 7i (RTX 4080 12GB) | Windows x86 | 32 GB | Comfortable | 39 t/s | Fits fully in VRAM (~12GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Dell XPS 14 (Core Ultra 7) | Windows x86 | 16 GB | Tight Fit | 9.7 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| Lenovo ThinkPad E14 (Budget Setup) | Windows x86 | 8 GB | Unsupported | — | Not enough memory to load the 4-bit weights (~7.2GB) plus overhead. |
| Acer Swift 14 AI (Snapdragon X Plus) | Windows ARM | 16 GB | Tight Fit | 11 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| Custom Workstation PC (RTX 4090 24GB) | Linux | 64 GB | Comfortable | 91 t/s | Fits fully in VRAM (~24GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Legacy Budget Desktop (GTX 1650 4GB) | Windows x86 | 16 GB | CPU Offload | 3.9 t/s | Exceeds the 4GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Apple MacBook Pro 16-inch (M4 Max 128GB) | macOS | 128 GB | Comfortable | 49.3 t/s | Fits fully in GPU-usable memory (~96GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple MacBook Pro 14-inch (M4 Pro 48GB) | macOS | 48 GB | Comfortable | 24.6 t/s | Fits fully in GPU-usable memory (~36GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple MacBook Pro 14-inch (M4 Base 24GB) | macOS | 24 GB | Comfortable | 10.8 t/s | Fits fully in GPU-usable memory (~16.1GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple MacBook Air 15-inch (M3 16GB) | macOS | 16 GB | Comfortable | 9 t/s | Fits fully in GPU-usable memory (~10.7GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple MacBook Air 13-inch (M3 8GB Base) | macOS | 8 GB | Unsupported | — | Not enough memory to load the 4-bit weights (~7.2GB) plus overhead. |
| Apple MacBook Air 13-inch (M2 16GB) | macOS | 16 GB | Comfortable | 9 t/s | Fits fully in GPU-usable memory (~10.7GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple MacBook Air (M1 16GB Legacy) | macOS | 16 GB | Comfortable | 6.2 t/s | Fits fully in GPU-usable memory (~10.7GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple MacBook Pro 16-inch (M3 Max 128GB) | macOS | 128 GB | Comfortable | 36.1 t/s | Fits fully in GPU-usable memory (~96GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple MacBook Pro 14-inch (M3 Pro 36GB) | macOS | 36 GB | Comfortable | 13.5 t/s | Fits fully in GPU-usable memory (~24.1GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple Mac Studio (M1 Ultra 64GB) | macOS | 64 GB | Comfortable | 72.2 t/s | Fits fully in GPU-usable memory (~48GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Samsung Galaxy Book4 Edge (Snapdragon X Elite) | Windows ARM | 16 GB | Tight Fit | 11 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| Lenovo Yoga Slim 7x Copilot+ | Windows ARM | 32 GB | Comfortable | 12.2 t/s | Fits fully in GPU-usable memory (~19.2GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Microsoft Surface Pro 11 Copilot+ (OLED) | Windows ARM | 32 GB | Comfortable | 12.2 t/s | Fits fully in GPU-usable memory (~19.2GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Dell XPS 13 Copilot+ (Snapdragon X Elite) | Windows ARM | 16 GB | Tight Fit | 11 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| Custom Workstation PC (RTX 4080 Super 16GB) | Linux / Windows | 32 GB | Comfortable | 66.4 t/s | Fits fully in VRAM (~16GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Custom AI Workstation (RTX 4070 Ti Super 16GB) | Linux / Windows | 32 GB | Comfortable | 60.7 t/s | Fits fully in VRAM (~16GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Desktop PC (RTX 4070 Super 12GB) | Windows / Linux | 32 GB | Comfortable | 45.5 t/s | Fits fully in VRAM (~12GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Budget AI Workstation (RTX 4060 Ti 16GB) | Linux / Windows | 32 GB | Comfortable | 26 t/s | Fits fully in VRAM (~16GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Mainstream Desktop (RTX 4060 8GB) | Windows / Linux | 32 GB | CPU Offload | 4.6 t/s | Exceeds the 8GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Used AI Powerhouse PC (RTX 3090 24GB) | Linux / Windows | 64 GB | Comfortable | 84.5 t/s | Fits fully in VRAM (~24GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Budget Workhorse PC (RTX 3060 12GB) | Windows / Linux | 16 GB | Comfortable | 32.5 t/s | Fits fully in VRAM (~12GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Lenovo Legion 5i (RTX 4060 Laptop 8GB) | Windows x86 | 16 GB | CPU Offload | 8.1 t/s | Exceeds the 8GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| ASUS TUF Gaming A15 (RTX 4060 Laptop 8GB) | Windows x86 | 16 GB | CPU Offload | 8.1 t/s | Exceeds the 8GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Acer Predator Helios Neo 16 (RTX 4070 Laptop) | Windows x86 | 32 GB | CPU Offload | 8.1 t/s | Exceeds the 8GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| HP Victus 16 (RTX 4050 Laptop 6GB) | Windows x86 | 16 GB | CPU Offload | 8.1 t/s | Exceeds the 6GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Dell Alienware m16 R2 (RTX 4070 8GB) | Windows x86 | 32 GB | CPU Offload | 8.1 t/s | Exceeds the 8GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| ASUS ROG Zephyrus G16 (AMD AI 9 + RTX 4070) | Windows x86 | 32 GB | CPU Offload | 10.8 t/s | Exceeds the 8GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Lenovo IdeaPad Pro 5 (Ryzen 7 8845HS) | Windows / Linux | 32 GB | Comfortable | 9.2 t/s | Fits fully in GPU-usable memory (~19.2GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Custom Workstation PC (Radeon RX 7900 XTX 24GB) | Linux / Windows | 64 GB | Comfortable | 86.7 t/s | Fits fully in VRAM (~24GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Desktop PC (Radeon RX 7800 XT 16GB) | Linux / Windows | 32 GB | Comfortable | 56.3 t/s | Fits fully in VRAM (~16GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Budget Workstation (Radeon RX 7600 XT 16GB) | Linux / Windows | 32 GB | Comfortable | 26 t/s | Fits fully in VRAM (~16GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| ASUS Zenbook S 14 (Lunar Lake Core Ultra 7) | Windows x86 | 32 GB | Comfortable | 12.3 t/s | Fits fully in GPU-usable memory (~19.2GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Lenovo Yoga Slim 7i Aura (Core Ultra 7 256V) | Windows x86 | 16 GB | Tight Fit | 11.1 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| Custom PC (Intel Arc A770 16GB) | Linux / Windows | 32 GB | Comfortable | 50.6 t/s | Fits fully in VRAM (~16GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Homelab AI Server (Dual RTX 3090 48GB VRAM) | Linux (Ubuntu Server) | 128 GB | Comfortable | 84.5 t/s | Fits fully in VRAM (~48GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Lenovo ThinkPad X1 Carbon Gen 12 | Windows x86 / Linux | 32 GB | Comfortable | 10.8 t/s | Fits fully in GPU-usable memory (~19.2GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Flagship AI Workstation (RTX 5090 32GB) | Linux / Windows | 64 GB | Comfortable | 161.8 t/s | Fits fully in VRAM (~32GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| High-End AI Rig (RTX 5080 16GB) | Linux / Windows | 64 GB | Comfortable | 86.7 t/s | Fits fully in VRAM (~16GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Next-Gen AI Laptop (Ryzen AI Max+ 395 128GB) | Linux / Windows | 128 GB | Comfortable | 23.1 t/s | Fits fully in GPU-usable memory (~96GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple Mac mini (M6, 16GB, 2026) | macOS | 16 GB | Comfortable | 13.8 t/s | Fits fully in GPU-usable memory (~10.7GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple Mac mini (M6, 32GB, 2026) | macOS | 32 GB | Comfortable | 15.3 t/s | Fits fully in GPU-usable memory (~21.4GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple Mac mini (M5 Pro, 48GB, 2026) | macOS | 48 GB | Comfortable | 27.7 t/s | Fits fully in GPU-usable memory (~36GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple Mac mini (M5 Pro, 64GB, 2026) | macOS | 64 GB | Comfortable | 27.7 t/s | Fits fully in GPU-usable memory (~48GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple MacBook Pro 14-inch (M5, 32GB, 2025) | macOS | 32 GB | Comfortable | 13.8 t/s | Fits fully in GPU-usable memory (~21.4GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple Mac Studio (M5 Max, 128GB, 2026) | macOS | 128 GB | Comfortable | 55.4 t/s | Fits fully in GPU-usable memory (~96GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple Mac Studio (M5 Ultra, 256GB, 2026) | macOS | 256 GB | Comfortable | 108.3 t/s | Fits fully in GPU-usable memory (~192GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple Mac Studio (M5 Ultra, 512GB, 2026) | macOS | 512 GB | Comfortable | 108.3 t/s | Fits fully in GPU-usable memory (~384GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Apple Mac Studio (M3 Ultra, 512GB, 2025) | macOS | 512 GB | Comfortable | 73.9 t/s | Fits fully in GPU-usable memory (~384GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| NVIDIA DGX Spark (GB10, 128GB) | DGX OS (Linux) | 128 GB | Comfortable | 24.6 t/s | Fits fully in GPU-usable memory (~110GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Desktop with NVIDIA RTX 5070 Ti (16GB) | Windows / Linux | 32 GB | Comfortable | 80.9 t/s | Fits fully in VRAM (~16GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Desktop with NVIDIA RTX 5060 Ti (16GB) | Windows / Linux | 32 GB | Comfortable | 40.4 t/s | Fits fully in VRAM (~16GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Desktop with AMD Radeon RX 9070 XT (16GB) | Windows / Linux | 32 GB | Comfortable | 57.8 t/s | Fits fully in VRAM (~16GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Desktop with AMD Radeon RX 9060 XT (16GB) | Windows / Linux | 32 GB | Comfortable | 28.9 t/s | Fits fully in VRAM (~16GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Desktop with Intel Arc B580 (12GB) | Windows / Linux | 32 GB | Comfortable | 41.2 t/s | Fits fully in VRAM (~12GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| ASUS Zenbook A16 (Snapdragon X2 Elite Extreme, 48GB) | Windows 11 (ARM) | 48 GB | Comfortable | 20.6 t/s | Fits fully in GPU-usable memory (~28.8GB) with room for a longer context. Speed is a bandwidth-based estimate. |
| Snapdragon X2 Elite Laptop (32GB) | Windows 11 (ARM) | 32 GB | Comfortable | 13.7 t/s | Fits fully in GPU-usable memory (~19.2GB) with room for a longer context. Speed is a bandwidth-based estimate. |
Guides that cover Gemma 4 12B
Step-by-step setup, memory sizing and quantization choices from the BFWR guide library.
- LLM Context Windows: What They Cost You in VRAMThe KV-cache arithmetic behind the VRAM calculator.Read the guide
- How Much RAM and VRAM Do You Need to Run Local LLMs?Model weights, KV cache and OS overhead, tier by tier.Read the guide
- Quantization Guide: Q4_K_M vs Q8_0 vs FP8What each quant level costs you in memory and quality.Read the guide
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