Gemma 3 4B IT — Hardware Requirements & GPU Compatibility
Vision
Gemma 3 4B IT is a small, roughly 4.3-billion-parameter model from Google's earlier Gemma 3 generation, tuned for instruction-following and chat. It accepts image input alongside text, so it can handle visual question answering and image-grounded prompts in addition to text-only conversation. At this size, the model is comfortable on modest consumer hardware, running even on integrated graphics at low quantization, making it one of the most accessible vision-capable models for local use. Released on February 20, 2025, Gemma 3 4B IT predates the Gemma 4 family and is distributed under Google's Gemma license terms rather than a standard open-source license. Its compact size and image-input support make it a practical choice for lightweight, on-device multimodal applications.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~231 tok/s · 32K)Macs from 32 GB (~30 tok/s)
Score 64#87 of 92 open#30 of 34 that fit 24 GBGemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
64.0
- 90% range 56.8–71.3
- #87 of 92 open models
- #30 of 34 that fit 24 GB
Run Gemma 3 4B IT
Best fit for your MacBook Air 15" M2 (16 GB). Every other method, OS and quant is in the step-by-step guide.
Recommended for your MacBook Air 15" M2 (16 GB)
ollama run gemma3:4bQ4_K_M fits fully · 7.5 GB free · 16K context
Download: gemma-3-4b-it.Q4_K_M.gguf2.49 GBMaziyarPanahi
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (7 GB headroom) · Q4_K_M
- Generation speed
- ~82 tok/s
- generation speed
- Cost per 1M output tokens
- $0.23
- per 1M output tokens
How Much VRAM Does Gemma 3 4B IT Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On your device | |
|---|---|---|---|---|
| 2.0 GB | 6.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~35 tok/s | |
| 2.1 GB | 6.7 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~34 tok/s | |
| 2.3 GB | 6.9 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~30 tok/s | |
| 2.4 GB | 7 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~30 tok/s | |
| 2.8 GB | 7.5 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~25 tok/s | |
| 3.4 GB | 8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~21 tok/s | |
| 3.9 GB | 8.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~18 tok/s | |
| 4.7 GB | 9.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~15 tok/s |
Selected Q4_K_M
4-bit medium quantization5.0GBat 16K context
- Weights
- 2.2 GB
- KV cache
- 2.5 GB
- Overhead
- 0.3 GB
Fits MacBook Air 15" M2 (16 GB) · 7.5 GB free · ~25 tok/s
≥50 tok/s needs ≥218 GB/s · RTX 5090 ~410, RTX 3090 Ti ~231
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 176 devices with unified memory can run Gemma 3 4B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
≤ 11 GB
≤ 11 GB tier →fits · 3.0 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~174Simulate output on RTX 3080RTX 2080 Ti~141Simulate output on RTX 2080 TiRTX 3070 Ti~139Simulate output on RTX 3070 TiM28 GB~25Simulate output on Mac Mini M2 (8 GB)Orin Nano~23Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~17Simulate output on iPhone 1712 GB
12 GB tier →fits · 7.0 GB free · up to 32K ctx
10 GPUs · 1 device family
RTX 3080 Ti~209Simulate output on RTX 3080 TiRTX 5070~154Simulate output on RTX 5070RTX 4070~115Simulate output on RTX 4070iPhone~19Simulate output on Apple iPhone 17 Pro20 GB
fits · 15.0 GB free · up to 32K ctx
1 GPU
RX 7900 XT~169Simulate output on RX 7900 XT
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Gemma 3 4B IT?
The llmrun Score merges 6 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the 24 GB tier
- Gemma 3 4B IT
- Other models
- Best Score for its VRAM
#30 of 34 that fit 24 GB12 of 34 fit your MacBook Air 15" M2 (16 GB) (16 GB)
Each dot is a model at the best quant that fits a 24 GB GPU. The line joins the models with the best Score for their VRAM; a dot below it is beaten by a model that needs no more memory.
Full 24 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 7 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Mar 2025Independent
Independent23.2%
#92/97 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1303.3
#104/186 open
- AIME 2024/2025
Epoch AI · Mar 2025Independent
Independent7.5%
#63/85 open
- MMLU-Pro
TIGER-LabIndependent
Independent43.6%
#91/144 open
- Chess Puzzles
Epoch AI · Mar 2025Independent
Independent0.0%
#51/67 open
- DTBench
Epoch AI · Mar 2025Lab-reported
Lab-reported50.9%
#58/68 open
In the 24 GB class
34 models fit 24 GBSame Score, only models that fit 24 GB at their best quant: the ranking a 24 GB owner actually chooses from.
- #1
Qwen3.8 27BAlibaba
121.5
~38 tok/s · 17.9 GB
- #2
Qwen3.6 27BAlibaba
111.4
~38 tok/s · 17.9 GB
- #3
Qwen3.6 35B A3BAlibaba
106.9
~151 tok/s · 22.0 GB
- #4
Gemma 4 31B ITGoogle
106.1
~32 tok/s · 21.7 GB
- #5
Qwen3.5 35B A3BAlibaba
105.6
~151 tok/s · 22.0 GB
- #6
Gemma 4 26B A4B ITGoogle
105.4
~171 tok/s · 16.5 GB
Alternatives to Gemma 3 4B IT
Gemma 3 family · 20 models →Official models with an llmrun Score, each answering one question; numbers are at the best quant that fits a 24 GB GPU.
Best alternative at this size
Qwen2.5 7B Instruct
Alibaba
- 1.1 higher Score
- 0.8 GB more VRAM
- 99 tok/s slower
Score 65.1 · 5.8 GB · ~131 tok/s
Much faster, similar quality
Llama 3.2 1B Instruct
Meta
- 6 lower Score
- 2.7 GB less VRAM
- 568 tok/s faster
Score 58.0 · 2.3 GB · ~799 tok/s
Other vendor
Qwen3.5 9B
Alibaba
- 35.4 higher Score
- 1.6 GB more VRAM
- 128 tok/s slower
Score 99.4 · 6.6 GB · ~103 tok/s
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3 8BAlibaba | 93.8 | 7.6 GB | ~119 | similar score |
| Llama 3.1 8B InstructMeta | 62.5 | 7.9 GB | ~124 | similar score |
| Meta Llama 3 8B InstructMeta | 61.7 | 7.9 GB | ~124 | similar score |
| Granite Vision 4.1 4BIBM | — | 4 GB | ~228 | same size |
| Qwen3 VL 4B InstructAlibaba | — | 4.5 GB | ~208 | same size |
| MiniCPM V 4OpenBMB | — | 3.1 GB | ~236 | same size |
| Phi 3.5 Vision InstructMicrosoft | — | 9.2 GB | ~183 | same size |
| Nanbeige4.2 3BNanbeige | — | 3.5 GB | ~227 | same size |
| Agents A1 4BInternScience | — | 3.4 GB | ~205 | same size |
| Gemma 3 1B ITGoogle | 44.6 | 2.1 GB | ~993 | same family |
Specifications
- Publisher
- Family
- Gemma 3
- Parameters
- 4.3B
- Release Date
- 2025-02-20
- License
- Gemma Terms
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
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. Q4_K_L (2.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma 3 4B IT on a Mac?
Yes — Mac Mini M1 (8 GB) and 155 other Macs can run Gemma 3 4B IT. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 3 4B IT locally?
Yes — Gemma 3 4B IT can run locally on consumer hardware. 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 ~1690 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 ÷ 2.84 × 0.65 = ~1831 tok/s
Estimated speed at Q4_K_M (2.8 GB)
Real-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?
52 consumer GPUs can run Gemma 3 4B IT at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Gemma 3 4B IT?
176 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.