Gemma 3 1B IT — Hardware Requirements & GPU Compatibility
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Google Gemma 3 1B IT is a 1-billion parameter instruction-tuned model from Google's Gemma 3 family. It is an ultra-compact text-only chat model designed for deployment on minimal hardware, including low-VRAM GPUs and edge devices. The model handles basic conversational tasks, simple instruction following, and lightweight text generation. It can run on virtually any modern GPU and even on CPU-only setups with acceptable latency. Released under the Gemma license.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~993 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~109 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 3 1B IT
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run gemma3:1bQ4_K_M fits fully on the RTX 4090 · 21.9 GB free · 16K context
Download: gemma-3-1b-it-Q4_K_M.gguf0.81 GBggml-org
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (9 GB headroom) · Q4_K_M
- Generation speed
- ~899 tok/s
- generation speed
- Cost per 1M output tokens
- $0.038
- per 1M output tokens
How Much VRAM Does Gemma 3 1B IT Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.5 GB | 3.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~1394 tok/s | |
| 0.5 GB | 3.6 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~1365 tok/s | |
| 0.5 GB | 3.6 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~1213 tok/s | |
| 0.6 GB | 3.6 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~1191 tok/s | |
| 0.7 GB | 3.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~993 tok/s | |
| 0.8 GB | 3.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~840 tok/s | |
| 0.9 GB | 4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~720 tok/s | |
| 1.1 GB | 4.2 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~596 tok/s |
Selected Q4_K_M
4-bit medium quantization2.1GBat 16K context
- Weights
- 0.2 GB
- KV cache
- 1.6 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.9 GB free · ~993 tok/s
≥50 tok/s needs ≥51 GB/s · RTX 5090 ~1765, RTX 3090 Ti ~993
Which Hardware Can Run Gemma 3 1B IT?
Q4_K_M · 0.7 GBGemma 3 1B IT (Q4_K_M) requires 0.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Gemma 3 1B IT, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.9 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~749Simulate output on RTX 3080RTX 2080 Ti~607Simulate output on RTX 2080 TiRTX 3070 Ti~599Simulate output on RTX 3070 TiM3*8 GB~109Simulate output on iMac 24" M3 (8 GB)Orin Nano~101Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~72Simulate output on iPhone 1720 GB
fits · 17.9 GB free · up to 32K ctx
1 GPU
RX 7900 XT~727Simulate output on RX 7900 XT32–40 GB
fits · 29.9 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1765Simulate output on RTX 5090M5 Max36 GB~488Simulate output on Mac Studio M5 Max (36 GB)M532 GB~163Simulate output on MacBook Air 13" M5 (32 GB)Orin~202Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~133Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Gemma 3 1B IT?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 3 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- GPQA Diamond
Epoch AI · Mar 2025Independent
Independent20.0%
#96/100 open
- AIME 2024/2025
Epoch AI · Mar 2025Independent
Independent1.1%
#81/88 open
- MMLU-Pro
TIGER-LabIndependent
Independent14.7%
#139/146 open
Alternatives to Gemma 3 1B IT
Gemma 3 family · 20 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| InternVL3 5 1BOpenGVLab | — | 2.2 GB | ~936 | same size |
| InternVL3 1BOpenGVLab | — | 2 GB | ~1057 | same size |
| InternVL2 1BOpenGVLab | — | 2 GB | ~1057 | same size |
| DiffuRefill 1BAsilarkness | — | 2.6 GB | ~252 | same size |
| Crowd V1Vxtzq | — | 3.7 GB | ~295 | same size |
| LLaMA3.1 8B Instruct DFlash UltraChatz-lab | — | 2.7 GB | ~269 | same size |
Specifications
- Publisher
- Family
- Gemma 3
- Parameters
- 1000M
- Context Length
- 32,768 tokens
- Release Date
- 2025-03-10
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does Gemma 3 1B IT need?
Gemma 3 1B IT requires 0.7 GB of VRAM at Q4_K_M, or 2.2 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1000M × 4.8 bits ÷ 8 = 0.6 GB
KV Cache + Overhead ≈ 0.1 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 1B IT?
For Gemma 3 1B IT, Q4_K_M (0.7 GB) offers the best balance of quality and VRAM usage. Q4_K_L (0.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma 3 1B IT on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Gemma 3 1B IT. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 3 1B IT locally?
Yes — Gemma 3 1B IT can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 3 1B IT?
At Q4_K_M, Gemma 3 1B IT can reach ~7273 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~993 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 ÷ 0.66 × 0.65 = ~7879 tok/s
Estimated speed at Q4_K_M (0.7 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 1B IT?
At Q4_K_M, the download is about 0.60 GB. The full-precision BF16 version is 2.00 GB. The smallest option (IQ2_XXS) is 0.27 GB.
Which GPUs can run Gemma 3 1B IT?
52 consumer GPUs can run Gemma 3 1B 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 1B IT?
163 devices with unified memory can run Gemma 3 1B IT at Q4_K_M (0.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.