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  1. Home
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  3. Google Gemma 2 27B It

Google Gemma 2 27B It

54GB VRAM (FP16)
27B parametersReleased 2025-018,192 token context

Minimum VRAM

54GB

FP16 (full model) • Q4 option ≈ 14GB

Best Performance

AMD Instinct MI300X

~160 tok/s • FP16

Most Affordable

Apple M4 Pro

FP16 • ~8 tok/s • From $1,999

Decision actions

AMD Instinct MI300X buy options →NVIDIA H200 SXM 141GB buy options →NVIDIA H100 SXM5 80GB buy options →Best GPU guides →Prebuilt systems →Local AI builds →

VRAM requirements at a glance

Q4 minimum
14GB
Q4_K_M
14GB
Q5_K_M
21GB
Q8 minimum
27GB
FP16 minimum
54GB

Quick answer: Google Gemma 2 27B It needs roughly 14GB VRAM for Q4_K_M and 21GB for Q5_K_M. Use Q8 (27GB) or FP16 (54GB) for higher quality output.

Full-model (FP16) requirements are shown by default. Quantized builds like Q4 trade accuracy for lower VRAM usage.


Compatible GPUs

Filter by quantization, price, and VRAM to compare performance estimates.

ℹ️Speeds are estimates based on hardware specs. Actual performance depends on software configuration. Learn more

Showing FP16 compatibility. Switch tabs to explore other quantizations.

GPUSpeedVRAM RequirementTypical price
AMD Instinct MI300XEstimated
AMD
~160 tok/s
FP16
54GB VRAM used192GB total on card
$15,000View GPU →
NVIDIA H200 SXM 141GBEstimated
NVIDIA
~144 tok/s
FP16
54GB VRAM used141GB total on card
$35,000View GPU →
NVIDIA H100 SXM5 80GBEstimated
NVIDIA
~103 tok/s
FP16
54GB VRAM used80GB total on card
$30,000View GPU →
AMD Instinct MI250XEstimated
AMD
~100 tok/s
FP16
54GB VRAM used128GB total on card
$11,000View GPU →
NVIDIA H100 PCIe 80GBEstimated
NVIDIA
~66 tok/s
FP16
54GB VRAM used80GB total on card
$25,000View GPU →
RTX 5090Estimated
NVIDIA
~63 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used32GB total on card
$1,999View GPU →
NVIDIA A100 80GB SXM4Estimated
NVIDIA
~61 tok/s
FP16
54GB VRAM used80GB total on card
$11,000View GPU →
AMD Instinct MI210Estimated
AMD
~50 tok/s
FP16
54GB VRAM used64GB total on card
$6,000View GPU →
NVIDIA A100 40GB PCIeEstimated
NVIDIA
~47 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used40GB total on card
$9,000View GPU →
RTX 4090Estimated
NVIDIA
~38 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used24GB total on card
$1,599View GPU →
NVIDIA RTX 6000 AdaEstimated
NVIDIA
~37 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used48GB total on card
$6,999View GPU →
NVIDIA L40Estimated
NVIDIA
~35 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used48GB total on card
$7,999View GPU →
NVIDIA L40SEstimated
NVIDIA
~35 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used48GB total on card
$10,000View GPU →
RTX 5080Estimated
NVIDIA
~33 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$1,199View GPU →
RTX 3090Estimated
NVIDIA
~32 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used24GB total on card
$1,499View GPU →
RX 7900 XTXEstimated
AMD
~30 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used24GB total on card
$999View GPU →
AMD Radeon Pro W7900Estimated
AMD
~30 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used48GB total on card
$3,999View GPU →
RTX 5070 TiEstimated
NVIDIA
~30 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$799View GPU →
NVIDIA A6000Estimated
NVIDIA
~28 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used48GB total on card
$4,699View GPU →
RTX 4080 SuperEstimated
NVIDIA
~26 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$999View GPU →
RTX 3080Data coming soon
NVIDIA
~26 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used10GB total on card
$699View GPU →
NVIDIA A5000Estimated
NVIDIA
~26 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used24GB total on card
$2,399View GPU →
RTX 4080Estimated
NVIDIA
~26 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$1,199View GPU →
RX 7900 XTEstimated
AMD
~25 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used20GB total on card
$899View GPU →
RTX 4070 Ti SuperEstimated
NVIDIA
~24 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$799View GPU →
RTX 5070Data coming soon
NVIDIA
~22 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used12GB total on card
$599View GPU →
Apple M2 UltraEstimated
Apple
~22 tok/s
FP16
54GB VRAM used192GB total on card
$5,999View GPU →
RX 9070 XTEstimated
AMD
~21 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$599View GPU →
RX 7800 XTEstimated
AMD
~20 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$499View GPU →
RX 7900 GREEstimated
AMD
~19 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$649View GPU →
AMD Radeon Pro W7800Estimated
AMD
~19 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used32GB total on card
$2,499View GPU →
RTX 4070 TiData coming soon
NVIDIA
~19 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used12GB total on card
$799View GPU →
RTX 4070 SuperData coming soon
NVIDIA
~18 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used12GB total on card
$599View GPU →
RX 9070Estimated
AMD
~18 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$499View GPU →
Intel Arc A770 16GBEstimated
Intel
~18 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$349View GPU →
RTX 4070Data coming soon
NVIDIA
~17 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used12GB total on card
$599View GPU →
RX 6900 XTEstimated
AMD
~17 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$999View GPU →
RX 6800 XTEstimated
AMD
~17 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$649View GPU →
Intel Arc A750Data coming soon
Intel
~16 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used8GB total on card
$289View GPU →
NVIDIA A4000Estimated
NVIDIA
~16 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$999View GPU →
RTX 3070Data coming soon
NVIDIA
~16 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used8GB total on card
$499View GPU →
Intel Arc B580Data coming soon
Intel
~16 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used12GB total on card
$249View GPU →
Apple M4 MaxEstimated
Apple
~15 tok/s
FP16
54GB VRAM used128GB total on card
$3,999View GPU →
RX 7700 XTData coming soon
AMD
~14 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used12GB total on card
$449View GPU →
Intel Arc B570Data coming soon
Intel
~13 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used10GB total on card
$219View GPU →
Intel Arc Pro A60Data coming soon
Intel
~13 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used12GB total on card
$599View GPU →
NVIDIA L4Estimated
NVIDIA
~13 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used24GB total on card
$5,000View GPU →
RTX 3060 12GBData coming soon
NVIDIA
~12 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used12GB total on card
$329View GPU →
Apple M3 MaxEstimated
Apple
~11 tok/s
FP16
54GB VRAM used128GB total on card
$3,999View GPU →
Apple M2 MaxEstimated
Apple
~11 tok/s
FP16
54GB VRAM used96GB total on card
$3,199View GPU →
RTX 4060 Ti 16GBEstimated
NVIDIA
~11 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$499View GPU →
RTX 4060 Ti 8GBData coming soon
NVIDIA
~11 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used8GB total on card
$399View GPU →
RTX 4060Data coming soon
NVIDIA
~9 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used8GB total on card
$299View GPU →
RX 7600 XTEstimated
AMD
~9 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used16GB total on card
$329View GPU →
RX 7600Data coming soon
AMD
~9 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used8GB total on card
$269View GPU →
Intel Arc Pro A40Data coming soon
Intel
~9 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used6GB total on card
$399View GPU →
Apple M4 ProEstimated
Apple
~8 tok/s
FP16
54GB VRAM used64GB total on card
$1,999View GPU →
AMD Ryzen AI Max+ 395Estimated
AMD
~8 tok/s
FP16
54GB VRAM used128GB total on card
EnterpriseView GPU →
AMD Ryzen AI Max 385Estimated
AMD
~8 tok/s
FP16
54GB VRAM used128GB total on card
EnterpriseView GPU →
AMD Ryzen AI Max Pro 385Estimated
AMD
~8 tok/s
FP16
54GB VRAM used128GB total on card
EnterpriseView GPU →
Apple M2 ProEstimated
Apple
~6 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used32GB total on card
$1,999View GPU →
Apple M3 ProEstimated
Apple
~4 tok/s
FP16⚠ Insufficient VRAM
54GB VRAM used36GB total on card
$1,999View GPU →
Don't see your GPU? View all compatible hardware →
Best GPU Options for Google Gemma 2 27B It

Google Gemma 2 27B It 27B parametre içerir ve 14GB VRAM gerektirir - choose the best GPU for your needs

MinimumBudget
RTX 3080
VRAM10GB
Price$7
View on Amazon
RecommendedBest Value
AMD Instinct MI300X
VRAM192GB
Price$150
View on Amazon

For Better Performance

Run Google Gemma 2 27B It faster with AMD Instinct MI300X. For just $143 more, significantly boost your tokens/sec performance.

Browse All GPUsCompare RTX 3080 vs AMD Instinct MI300X
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Detailed Specifications

Hardware requirements and model sizes at a glance.

Technical details

Parameters
27,000,000,000 (27B)
Architecture
Transformer
Developer
—
Released
January 2025
Context window
8,192 tokens

Quantization support

Q4
14GB VRAM required • 14GB download
Q4_K_M
14GB VRAM required • 14GB download
Q5_K_M
21GB VRAM required • 27GB download
Q8
27GB VRAM required • 27GB download
FP16
54GB VRAM required • 54GB download

Hardware Requirements

ComponentMinimumRecommendedOptimal
VRAM14GB (Q4)27GB (Q8)54GB (FP16)
RAM21GB41GB68GB
Disk12GB22GB-
Model size14GB (Q4)27GB (Q8)54GB (FP16)
CPUModern CPU (Ryzen 5/Intel i5 or better)Modern CPU (Ryzen 5/Intel i5 or better)Modern CPU (Ryzen 5/Intel i5 or better)

Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data


Quantization requirement shortcuts
Built for high-intent queries like "Google Gemma 2 27B It q4 vram requirements".
Q4 VRAM usageQ4_K_M VRAM usageQ5_K_M VRAM usageQ8 VRAM usageFP16 VRAM usage
Model speed shortcuts
Direct answers for "Google Gemma 2 27B It speed on [GPU]" searches.
Google Gemma 2 27B It speed on Apple M4 Max
Q4 • ~40 tok/s
Google Gemma 2 27B It speed on RTX 4090
Q4 • ~99 tok/s
Google Gemma 2 27B It speed on RTX 5090
Q4 • ~165 tok/s
Google Gemma 2 27B It speed on RTX 5080
Q4 • ~87 tok/s
Google Gemma 2 27B It speed on NVIDIA L4
Q4 • ~33 tok/s
Best GPU buying guides →Compare prebuilt systems →Local AI build recipes →

Frequently Asked Questions

Common questions about running Google Gemma 2 27B It locally

What should I know before running Google Gemma 2 27B It?

This model delivers strong local performance when paired with modern GPUs. Use the hardware guidance below to choose the right quantization tier for your build.

How do I deploy this model locally?

Use runtimes like llama.cpp, text-generation-webui, or vLLM. Download the quantized weights from Hugging Face, ensure you have enough VRAM for your target quantization, and launch with GPU acceleration (CUDA/ROCm/Metal).

Which quantization should I choose?

Start with Q4 for wide GPU compatibility. Upgrade to Q8 if you have spare VRAM and want extra quality. FP16 delivers the highest fidelity but demands workstation or multi-GPU setups.

What is the difference between Q4, Q4_K_M, Q5_K_M, and Q8 quantization for Google Gemma 2 27B It?

Q4_K_M and Q5_K_M are GGUF quantization formats that balance quality and VRAM usage. Q4_K_M uses about 14GB VRAM. Q5_K_M uses about 21GB VRAM and keeps more accuracy. Q8 (~27GB) offers near-FP16 quality. Standard Q4 is the most memory-efficient option for Google Gemma 2 27B It.

Where can I download Google Gemma 2 27B It?

Official weights are available via Hugging Face. Quantized builds (Q4, Q8) can be loaded into runtimes like llama.cpp, text-generation-webui, or vLLM. Always verify the publisher before downloading.


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