Can NVIDIA GeForce RTX 4090 run Gemma 2 27B IT?

Yes, at Q5_K_L (21.7 GB) or lower. Higher quantizations like Q6_K (24.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Gemma 2 27B IT?

For Gemma 2 27B IT, Q4_K_M (18.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (18.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 9.0 GB.

Can I run Gemma 2 27B IT on a Mac?

Gemma 2 27B IT requires at least 9.0 GB at IQ2_XS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.

Can I run Gemma 2 27B IT locally?

Yes — Gemma 2 27B IT can run locally on consumer hardware. At Q4_K_M quantization it needs 18.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Gemma 2 27B IT?

At Q4_K_M, Gemma 2 27B IT can reach ~162 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~37 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

What's the download size of Gemma 2 27B IT?

At Q4_K_M, the download is about 16.34 GB. The full-precision Q8_0 version is 27.23 GB. The smallest option (IQ2_XS) is 8.17 GB.

Google·Gemma 2

Gemma 2 27B IT — Hardware Requirements & GPU Compatibility

Chat

Google Gemma 2 27B IT is a 27.2-billion parameter instruction-tuned model from Google's Gemma 2 generation. It is a text-only chat model optimized for conversational use, reasoning, and instruction following. Gemma 2 27B IT was one of the strongest openly available models in its size class at release. The model requires a GPU with at least 24GB of VRAM for quantized local inference. It is widely supported by popular inference engines and remains a strong choice for users seeking high-quality local chat without needing 70B-class hardware. Released under the Gemma license.

401.5K downloads 560 likes8K context

Specifications

Publisher: Google
Family: Gemma 2
Parameters: 27.2B
Context Length: 8,192 tokens
License: Gemma Terms

Get Started

HuggingFace

google/gemma-2-27b-it

How Much VRAM Does Gemma 2 27B IT Need?

Select a quantization to see compatible GPUs below.

Quantization	Bits	VRAM	+ Context	File Size	Quality
IQ2_XS	2.40	9.0 GB	—	8.17 GB	Importance-weighted 2-bit, extra small
IQ2_S	2.50	9.4 GB	—	8.51 GB	Importance-weighted 2-bit, small
IQ2_M	2.70	10.1 GB	—	9.19 GB	Importance-weighted 2-bit, medium
IQ3_XXS	3.10	11.6 GB	—	10.55 GB	Importance-weighted 3-bit
IQ3_XS	3.30	12.3 GB	—	11.23 GB	Importance-weighted 3-bit, extra small
Q2_K	3.40	12.7 GB	—	11.57 GB	2-bit quantization with K-quant improvements
IQ3_S	3.40	12.7 GB	—	11.57 GB	Importance-weighted 3-bit, small
Q3_K_S	3.50	13.1 GB	—	11.91 GB	3-bit small quantization
IQ3_M	3.60	13.5 GB	—	12.25 GB	Importance-weighted 3-bit, medium
Q3_K_M	3.90	14.6 GB	—	13.27 GB	3-bit medium quantization
Q3_K_L	4.10	15.3 GB	—	13.95 GB	3-bit large quantization
IQ4_XS	4.30	16.1 GB	—	14.63 GB	Importance-weighted 4-bit, compact
Q4_K_S	4.50	16.9 GB	—	15.32 GB	4-bit small quantization
Q4_K_M	4.80	18.0 GB	—	16.34 GB	4-bit medium quantization — most popular sweet spot
Q4_K_L	4.90	18.3 GB	—	16.68 GB	4-bit large quantization
Q5_K_S	5.50	20.6 GB	—	18.72 GB	5-bit small quantization
Q5_K_M	5.70	21.3 GB	—	19.40 GB	5-bit medium quantization — good quality/size tradeoff
Q5_K_L	5.80	21.7 GB	—	19.74 GB	5-bit large quantization
Q6_K	6.60	24.7 GB	—	22.46 GB	6-bit quantization, very good quality
Q8_0	8.00	29.9 GB	—	27.23 GB	8-bit quantization, near-lossless

Which GPUs Can Run Gemma 2 27B IT?

Q4_K_M · 18.0 GB

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Gemma 2 27B IT (Q4_K_M) requires 18.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 24+ GB is recommended. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Runs great

— Plenty of headroom

NVIDIA GeForce RTX 5090~65 tok/s

Decent

— Enough VRAM, may be tight

NVIDIA GeForce RTX 3090 Ti~37 tok/s NVIDIA GeForce RTX 4090~37 tok/s NVIDIA GeForce RTX 3090~34 tok/s AMD Radeon RX 7900 XTX~29 tok/s AMD Radeon RX 7900 XT~25 tok/s

Which Devices Can Run Gemma 2 27B IT?

Q4_K_M · 18.0 GB

21 devices with unified memory can run Gemma 2 27B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).

Related Models

Derivatives (3)

Gemma 2 27B IT GGUF Gemma 2 27B IT GGUF Gemma 2 27B IT GGUF

Frequently Asked Questions

How much VRAM does Gemma 2 27B IT need?: Gemma 2 27B IT requires 18.0 GB of VRAM at Q4_K_M, or 29.9 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 27.2B × 4.8 bits ÷ 8 = 16.3 GB
KV Cache + Overhead ≈ 1.7 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M
18.0 GB
Learn more about VRAM estimation →
Can NVIDIA GeForce RTX 4090 run Gemma 2 27B IT?: Yes, at Q5_K_L (21.7 GB) or lower. Higher quantizations like Q6_K (24.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Gemma 2 27B IT?: For Gemma 2 27B IT, Q4_K_M (18.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (18.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 9.0 GB.
VRAM requirement by quantization
IQ2_XS
9.0 GB~57%
Q2_K
12.7 GB~75%
Q3_K_L
15.3 GB~86%
Q4_K_M ★
18.0 GB~89%
Q4_K_L
18.3 GB~90%
Q8_0
29.9 GB~99%
★ Recommended — best balance of quality and VRAM usage.
Learn more about quantization →
Can I run Gemma 2 27B IT on a Mac?: Gemma 2 27B IT requires at least 9.0 GB at IQ2_XS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
Can I run Gemma 2 27B IT locally?: Yes — Gemma 2 27B IT can run locally on consumer hardware. At Q4_K_M quantization it needs 18.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 2 27B IT?: At Q4_K_M, Gemma 2 27B IT can reach ~162 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~37 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 18.0 × 0.55 = ~162 tok/s
Estimated speed at Q4_K_M (18.0 GB)
AMD Instinct MI300X
~162 tok/s
NVIDIA GeForce RTX 4090
~37 tok/s
NVIDIA H100 SXM
~121 tok/s
AMD Instinct MI250X
~100 tok/s
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
Learn more about tok/s estimation →
What's the download size of Gemma 2 27B IT?: At Q4_K_M, the download is about 16.34 GB. The full-precision Q8_0 version is 27.23 GB. The smallest option (IQ2_XS) is 8.17 GB.