Gemma 3 4B IT — Hardware Requirements & GPU Compatibility
Vision
Gemma 3 4B IT is a 4.3B-parameter open language model from Google in the Gemma 3 family. At Q4_K_M it needs about 2.84 GB of VRAM — see which GPUs and Macs can run it below.
2.3M downloads
1.4K likesSpecifications
- Publisher
- Family
- Gemma 3
- Parameters
- 4.3B
- Release Date
- 2025-02-20
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Gemma 3 4B IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.0 GB | — | 1.83 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.1 GB | — | 1.88 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.3 GB | — | 2.10 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.4 GB | — | 2.15 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.8 GB | — | 2.58 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.4 GB | — | 3.06 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.9 GB | — | 3.55 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.7 GB | — | 4.30 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 3 4B IT?
Q4_K_M · 2.8 GBGemma 3 4B IT (Q4_K_M) requires 2.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Gemma 3 4B IT?
Q4_K_M · 2.8 GB59 devices with unified memory can run Gemma 3 4B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Gemma 3 4B IT
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Gemma 3 4B IT need?
Gemma 3 4B IT requires 2.8 GB of VRAM at Q4_K_M, or 9.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 4.3B × 4.8 bits ÷ 8 = 2.6 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M2.8 GB- What's the best quantization for Gemma 3 4B IT?
For Gemma 3 4B IT, Q4_K_M (2.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (3.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.3 GB.
VRAM requirement by quantization
IQ2_XXS1.3 GBIQ3_XS1.9 GBQ4_02.4 GBIQ4_NL2.7 GBQ4_K_M ★2.8 GBBF169.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 3 4B IT on a Mac?
Gemma 3 4B IT requires at least 1.3 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 4B IT locally?
Yes — Gemma 3 4B IT can run locally on consumer hardware. At Q4_K_M quantization it needs 2.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 3 4B IT?
At Q4_K_M, Gemma 3 4B IT can reach ~1549 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~231 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 2.8 × 0.65 = ~1831 tok/s
Estimated speed at Q4_K_M (2.8 GB)
~1831 tok/s~231 tok/s~1831 tok/s~1549 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gemma 3 4B IT?
At Q4_K_M, the download is about 2.58 GB. The full-precision BF16 version is 8.60 GB. The smallest option (IQ2_XXS) is 1.18 GB.
- Which GPUs can run Gemma 3 4B IT?
50 consumer GPUs can run Gemma 3 4B IT at Q4_K_M (2.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 3 4B IT?
59 devices with unified memory can run Gemma 3 4B IT at Q4_K_M (2.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.