Google DeepMindGeneralLicense: Gemma Terms of Use
Google Gemma 2 27B
The 27B powerhouse capable of competing with 70B models on single 16GB–24GB GPUs.
Gemma 2 27B provides flagship-level reasoning and factual knowledge in an ultra-compact footprint. Thanks to its 27B size, it can fit onto 16GB GPUs with aggressive 3-bit/4-bit quantizations or run with uncompromised Q8 precision on 24GB GPUs.
Parameter Count27.2B
Context Window8k tokens
Min VRAM (Q4)16 GB
Recommended RAM20 GB+
⚡ Key Architectural Strengths
✓Competes with Llama 3 70B on standard MMLU and reasoning benchmarks
✓Fits onto a single RTX 3090/4090 with high context
✓Low latency response generation
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) | 16.5 GB | 18 GB | 20 GB | Fits inside 20GB–24GB VRAM or 24GB Unified Memory. |
| Q8_0 (High Precision) | 29 GB | 32 GB | 36 GB | Requires 36GB+ Unified Memory Mac or dual GPU system. |
| BF16 / FP16 (Unquantized) | 54.4 GB | 60 GB | 64 GB | Unquantized weights. |
⚡ 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 gemma2:27bLM Studio CLI:
lms load google/gemma-2-27b-it-GGUFvLLM Production Server:
vllm serve google/gemma-2-27b-it --dtype autollama.cpp Binary:
./llama-cli -m gemma-2-27b-it-Q4_K_M.gguf -p "Analyze this complex philosophical prompt" -c 8192Hardware 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 | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| HP Omnibook 5 OLED | Windows ARM | 32 GB | Tight Fit | 4.8 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| Apple MacBook Pro 16-inch (M4 Max) | macOS | 64 GB | Comfortable | 21.5 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 | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Apple Mac mini (M4 Pro, 24GB) | macOS | 24 GB | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Apple Mac Studio (M2 Ultra 192GB) | macOS | 192 GB | Comfortable | 31.5 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 | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Lenovo ThinkPad T14s Gen 5 (Snapdragon) | Windows ARM | 32 GB | Tight Fit | 4.8 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| ASUS Zenbook S 16 (Ryzen AI 9 HX 370) | Windows x86 | 32 GB | Tight Fit | 4.3 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| Dell XPS 13 (Intel Core Ultra 7 258V) | Windows x86 | 32 GB | Tight Fit | 4.8 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| ASUS ROG Zephyrus G16 (RTX 4070 Laptop) | Windows x86 | 32 GB | CPU Offload | 3.5 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 | CPU Offload | 3.5 t/s | Exceeds the 16GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Framework Laptop 13 (Ryzen 7 7840U) | Linux | 64 GB | Comfortable | 3.5 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 | CPU Offload | 3.5 t/s | Exceeds the 12GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Dell XPS 14 (Core Ultra 7) | Windows x86 | 16 GB | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Lenovo ThinkPad E14 (Budget Setup) | Windows x86 | 8 GB | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Acer Swift 14 AI (Snapdragon X Plus) | Windows ARM | 16 GB | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Custom Workstation PC (RTX 4090 24GB) | Linux | 64 GB | Comfortable | 39.7 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 | 1.7 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 | 21.5 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 | 10.8 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 | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Apple MacBook Air 15-inch (M3 16GB) | macOS | 16 GB | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Apple MacBook Air 13-inch (M3 8GB Base) | macOS | 8 GB | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Apple MacBook Air 13-inch (M2 16GB) | macOS | 16 GB | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Apple MacBook Air (M1 16GB Legacy) | macOS | 16 GB | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Apple MacBook Pro 16-inch (M3 Max 128GB) | macOS | 128 GB | Comfortable | 15.8 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 | 5.9 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 | 31.5 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 | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Lenovo Yoga Slim 7x Copilot+ | Windows ARM | 32 GB | Tight Fit | 4.8 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| Microsoft Surface Pro 11 Copilot+ (OLED) | Windows ARM | 32 GB | Tight Fit | 4.8 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| Dell XPS 13 Copilot+ (Snapdragon X Elite) | Windows ARM | 16 GB | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Custom Workstation PC (RTX 4080 Super 16GB) | Linux / Windows | 32 GB | CPU Offload | 3.8 t/s | Exceeds the 16GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Custom AI Workstation (RTX 4070 Ti Super 16GB) | Linux / Windows | 32 GB | CPU Offload | 3.8 t/s | Exceeds the 16GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Desktop PC (RTX 4070 Super 12GB) | Windows / Linux | 32 GB | CPU Offload | 3.5 t/s | Exceeds the 12GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Budget AI Workstation (RTX 4060 Ti 16GB) | Linux / Windows | 32 GB | CPU Offload | 2 t/s | Exceeds the 16GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Mainstream Desktop (RTX 4060 8GB) | Windows / Linux | 32 GB | CPU Offload | 2 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 | 36.9 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 | CPU Offload | 2 t/s | Exceeds the 12GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Lenovo Legion 5i (RTX 4060 Laptop 8GB) | Windows x86 | 16 GB | CPU Offload | 3.5 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 | 3.5 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 | 3.5 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 | 3.5 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 | 3.5 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 | 4.7 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 | Tight Fit | 3.6 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| Custom Workstation PC (Radeon RX 7900 XTX 24GB) | Linux / Windows | 64 GB | Comfortable | 37.8 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 | CPU Offload | 2.3 t/s | Exceeds the 16GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Budget Workstation (Radeon RX 7600 XT 16GB) | Linux / Windows | 32 GB | CPU Offload | 2 t/s | Exceeds the 16GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| ASUS Zenbook S 14 (Lunar Lake Core Ultra 7) | Windows x86 | 32 GB | Tight Fit | 4.8 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| Lenovo Yoga Slim 7i Aura (Core Ultra 7 256V) | Windows x86 | 16 GB | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Custom PC (Intel Arc A770 16GB) | Linux / Windows | 32 GB | CPU Offload | 3.5 t/s | Exceeds the 16GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Homelab AI Server (Dual RTX 3090 48GB VRAM) | Linux (Ubuntu Server) | 128 GB | Comfortable | 36.9 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 | Tight Fit | 4.2 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
| Flagship AI Workstation (RTX 5090 32GB) | Linux / Windows | 64 GB | Comfortable | 70.6 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 | CPU Offload | 3.8 t/s | Exceeds the 16GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Next-Gen AI Laptop (Ryzen AI Max+ 395 128GB) | Linux / Windows | 128 GB | Comfortable | 10.1 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 | Unsupported | — | Not enough memory to load the 4-bit weights (~16.5GB) plus overhead. |
| Apple Mac mini (M6, 32GB, 2026) | macOS | 32 GB | Comfortable | 6.7 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 | 12.1 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 | 12.1 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 | 6 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 | 24.2 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 | 47.3 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 | 47.3 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 | 32.3 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 | 10.8 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 | CPU Offload | 3.8 t/s | Exceeds the 16GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Desktop with NVIDIA RTX 5060 Ti (16GB) | Windows / Linux | 32 GB | CPU Offload | 3.5 t/s | Exceeds the 16GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Desktop with AMD Radeon RX 9070 XT (16GB) | Windows / Linux | 32 GB | CPU Offload | 3.8 t/s | Exceeds the 16GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Desktop with AMD Radeon RX 9060 XT (16GB) | Windows / Linux | 32 GB | CPU Offload | 3.5 t/s | Exceeds the 16GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| Desktop with Intel Arc B580 (12GB) | Windows / Linux | 32 GB | CPU Offload | 3.5 t/s | Exceeds the 12GB of VRAM, so some layers run from system RAM, which is much slower. Estimate only. |
| ASUS Zenbook A16 (Snapdragon X2 Elite Extreme, 48GB) | Windows 11 (ARM) | 48 GB | Comfortable | 9 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 | Tight Fit | 5.4 t/s | Fits with a modest context (4k-8k). Close other memory-heavy apps. Speed is an estimate. |
Guides that cover Google Gemma 2 27B
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
Not sure if Google Gemma 2 27B fits your specific setup?
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