Can NVIDIA GeForce RTX 4090 run Gemma 3 27B IT GGUF?

Yes, at Q6_K (23.9 GB) or lower. Higher quantizations like Q8_0 (28.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Gemma 3 27B IT GGUF?

For Gemma 3 27B IT GGUF, Q4_K_M (17.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (20.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.1 GB.

Can I run Gemma 3 27B IT GGUF on a Mac?

Gemma 3 27B IT GGUF requires at least 9.1 GB at IQ2_XXS, 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 3 27B IT GGUF locally?

Yes — Gemma 3 27B IT GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 17.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Gemma 3 27B IT GGUF?

At Q4_K_M, Gemma 3 27B IT GGUF can reach ~163 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 3 27B IT GGUF?

At Q4_K_M, the download is about 16.20 GB. The full-precision Q8_0 version is 27.00 GB. The smallest option (IQ2_XXS) is 7.43 GB.

Unsloth·Gemma·Gemma3ForConditionalGeneration

Gemma 3 27B IT GGUF — Hardware Requirements & GPU Compatibility

Vision

129.2K downloads 193 likes131K context

Based on Gemma 3 27B IT

Specifications

Publisher: Unsloth
Family: Gemma
Parameters: 27B
Architecture: Gemma3ForConditionalGeneration
Context Length: 131,072 tokens
Vocabulary Size: 262,208
License: Gemma Terms

Get Started

HuggingFace

unsloth/gemma-3-27b-it-GGUF

How Much VRAM Does Gemma 3 27B IT GGUF Need?

Select a quantization to see compatible GPUs below.

Quantization	Bits	VRAM	+ Context	File Size	Quality
IQ2_XXS	2.20	9.1 GB	95.1 GB	7.43 GB	Importance-weighted 2-bit, extreme compression — significant quality loss
IQ2_M	2.70	10.8 GB	96.8 GB	9.11 GB	Importance-weighted 2-bit, medium
IQ3_XXS	3.10	12.1 GB	98.1 GB	10.46 GB	Importance-weighted 3-bit
Q2_K	3.40	13.1 GB	99.2 GB	11.47 GB	2-bit quantization with K-quant improvements
Q3_K_S	3.50	13.5 GB	99.5 GB	11.81 GB	3-bit small quantization
Q3_K_M	3.90	14.8 GB	100.8 GB	13.16 GB	3-bit medium quantization
Q4_0	4.00	15.2 GB	101.2 GB	13.50 GB	4-bit legacy quantization
IQ4_XS	4.30	16.2 GB	102.2 GB	14.51 GB	Importance-weighted 4-bit, compact
Q4_1	4.50	16.9 GB	102.9 GB	15.19 GB	4-bit legacy quantization with offset
Q4_K_S	4.50	16.9 GB	102.9 GB	15.19 GB	4-bit small quantization
IQ4_NL	4.50	16.9 GB	102.9 GB	15.19 GB	Importance-weighted 4-bit, non-linear
Q4_K_M	4.80	17.9 GB	103.9 GB	16.20 GB	4-bit medium quantization — most popular sweet spot
Q5_K_S	5.50	20.2 GB	106.2 GB	18.56 GB	5-bit small quantization
Q5_K_M	5.70	20.9 GB	106.9 GB	19.24 GB	5-bit medium quantization — good quality/size tradeoff
Q6_K	6.60	23.9 GB	110.0 GB	22.27 GB	6-bit quantization, very good quality
Q8_0	8.00	28.7 GB	114.7 GB	27.00 GB	8-bit quantization, near-lossless

Which GPUs Can Run Gemma 3 27B IT GGUF?

Q4_K_M · 17.9 GB

Show professional

Gemma 3 27B IT GGUF (Q4_K_M) requires 17.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 24+ GB is recommended. Using the full 131K context window can add up to 86.0 GB, bringing total usage to 103.9 GB. 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~30 tok/s AMD Radeon RX 7900 XT~25 tok/s

Which Devices Can Run Gemma 3 27B IT GGUF?

Q4_K_M · 17.9 GB

21 devices with unified memory can run Gemma 3 27B IT GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).

Related Models

Frequently Asked Questions

How much VRAM does Gemma 3 27B IT GGUF need?: Gemma 3 27B IT GGUF requires 17.9 GB of VRAM at Q4_K_M, or 28.7 GB at Q8_0. Full 131K context adds up to 86.0 GB (103.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 27B × 4.8 bits ÷ 8 = 16.2 GB
KV Cache + Overhead ≈ 1.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 87.7 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M
17.9 GB
Q4_K_M + full context
103.9 GB
Learn more about VRAM estimation →
Can NVIDIA GeForce RTX 4090 run Gemma 3 27B IT GGUF?: Yes, at Q6_K (23.9 GB) or lower. Higher quantizations like Q8_0 (28.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Gemma 3 27B IT GGUF?: For Gemma 3 27B IT GGUF, Q4_K_M (17.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (20.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.1 GB.
VRAM requirement by quantization
IQ2_XXS
9.1 GB~53%
Q3_K_S
13.5 GB~77%
Q4_1
16.9 GB~88%
Q4_K_M ★
17.9 GB~89%
Q5_K_S
20.2 GB~92%
Q8_0
28.7 GB~99%
★ Recommended — best balance of quality and VRAM usage.
Learn more about quantization →
Can I run Gemma 3 27B IT GGUF on a Mac?: Gemma 3 27B IT GGUF requires at least 9.1 GB at IQ2_XXS, 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 3 27B IT GGUF locally?: Yes — Gemma 3 27B IT GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 17.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 3 27B IT GGUF?: At Q4_K_M, Gemma 3 27B IT GGUF can reach ~163 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 ÷ 17.9 × 0.55 = ~163 tok/s
Estimated speed at Q4_K_M (17.9 GB)
AMD Instinct MI300X
~163 tok/s
NVIDIA GeForce RTX 4090
~37 tok/s
NVIDIA H100 SXM
~122 tok/s
AMD Instinct MI250X
~101 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 3 27B IT GGUF?: At Q4_K_M, the download is about 16.20 GB. The full-precision Q8_0 version is 27.00 GB. The smallest option (IQ2_XXS) is 7.43 GB.