Gemma 2 9B IT — Hardware Requirements & GPU Compatibility
Chat
Google Gemma 2 9B IT is a 9.2-billion parameter instruction-tuned model from Google's Gemma 2 series. It is a text-only chat model optimized for conversational tasks, instruction following, and general-purpose assistance. At release, it was recognized for delivering unusually strong performance relative to its parameter count. The model runs efficiently on consumer GPUs with 8-12GB of VRAM in quantized formats, making it accessible on mainstream hardware. It is a popular choice for local inference among users who want strong quality without the VRAM demands of larger models. Released under the Gemma license.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 8K)Best on 24 GB (Q4_K_M · ~107 tok/s · 8K)Macs from 8 GB (IQ2_S · ~23 tok/s)
Score 68#99 of 106 open#30 of 36 that fit 24 GBGemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
68.0
- 90% range 60.4–75.5
- #99 of 106 open models
- #30 of 36 that fit 24 GB
Run Gemma 2 9B 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 gemma2:9b-instruct-q4_K_MQ4_K_M fits fully on the RTX 4090 · 16.7 GB free · 8K context
Download: gemma-2-9b-it-Q4_K_M.gguf5.76 GBbartowski
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) (4 GB headroom) · Q4_K_M
- Generation speed
- ~97 tok/s
- generation speed
- Cost per 1M output tokens
- $0.35
- per 1M output tokens
How Much VRAM Does Gemma 2 9B IT Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 8K context · ~152 tok/s | |
| 4.5 GB | — | 3-bit small — 3-bit small quantization | Fits: 8K context · ~147 tok/s | |
| 5.0 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 8K context · ~132 tok/s | |
| 5.1 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 8K context · ~129 tok/s | |
| 6.1 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~107 tok/s | |
| 7.2 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 8K context · ~91 tok/s | |
| 8.4 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 8K context · ~78 tok/s | |
| 10.2 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 8K context · ~64 tok/s |
Selected Q4_K_M
4-bit medium quantization7.3GBat 8K context
- Weights
- 5.4 GB
- KV cache
- 1.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.8 GB free · ~107 tok/s
≥50 tok/s needs ≥469 GB/s · RTX 5090 ~191, RTX 3090 Ti ~107
Which Hardware Can Run Gemma 2 9B IT?
Q4_K_M · 6.1 GBGemma 2 9B IT (Q4_K_M) requires 6.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Gemma 2 9B IT, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 0.8 GB free · up to 8K ctx
16 GPUs · 1 device family
RTX 3080~81Simulate output on RTX 3080RTX 2080 Ti~66Simulate output on RTX 2080 TiRTX 3070 Ti~65Simulate output on RTX 3070 TiOrin Nano~11Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.8 GB free · up to 8K ctx
1 GPU
RX 7900 XT~79Simulate output on RX 7900 XT32–40 GB
fits · 24.8 GB free · up to 8K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~191Simulate output on RTX 5090M5 Max36 GB~53Simulate output on Mac Studio M5 Max (36 GB)M532 GB~18Simulate output on MacBook Air 13" M5 (32 GB)Orin~22Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~14Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Gemma 2 9B IT?
The llmrun Score merges 3 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the 24 GB tier
- Gemma 2 9B IT
- Other models
- Best Score for its VRAM
#30 of 36 that fit 24 GB
Each dot is a model at the best quant that fits a 24 GB GPU. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.
Full 24 GB ranking →Benchmarks
Here are all 6 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 · Jun 2024Independent
Independent27.5%
#88/100 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1267.0
#125/188 open
- AIME 2024/2025
Epoch AI · Jun 2024Independent
Independent0.6%
#84/88 open
- MMLU-Pro
TIGER-LabIndependent
Independent52.1%
#73/146 open
- MATH Level 5
Epoch AI · Jun 2024Independent
Independent21.0%
#31/39 open
- MMLU
Epoch AI · Jun 2024Lab-reported
Lab-reported72.1%
#25/85 open
In the 24 GB class
36 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
122.0
~38 tok/s · 17.9 GB
- #2
Qwen3.6 27BAlibaba
111.2
~38 tok/s · 17.9 GB
- #3
Qwen3.6 35B A3BAlibaba
106.8
~151 tok/s · 22.0 GB
- #4
Gemma 4 31B ITGoogle
106.7
~32 tok/s · 21.7 GB
- #5
Gemma 4 26B A4B ITGoogle
105.8
~171 tok/s · 16.5 GB
- #6
Qwen3.5 35B A3BAlibaba
105.8
~151 tok/s · 22.0 GB
Alternatives to Gemma 2 9B IT
Gemma 2 family · 21 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
Qwen3.5 9B
Alibaba
- 31.4 higher Score
- 0.6 GB less VRAM
- 4 tok/s slower
Score 99.4 · 6.6 GB · ~103 tok/s
Much faster, similar quality
Gemma 4 26B A4B IT
Google
- 37.8 higher Score
- 9.2 GB more VRAM
- 64 tok/s faster
Score 105.8 · 16.5 GB · ~171 tok/s
Other vendor
GPT OSS 20B
OpenAI
- 28.4 higher Score
- 5.9 GB more VRAM
- 82 tok/s faster
Score 96.4 · 13.1 GB · ~190 tok/s
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3 14BAlibaba | 98.0 | 11.9 GB | ~69 | similar score |
| Qwen3 8BAlibaba | 93.9 | 7.6 GB | ~119 | similar score |
| Phi 4Microsoft | 83.9 | 12.5 GB | ~69 | similar score |
| Gemma 3 12B ITGoogle | 74.2 | 10.9 GB | ~82 | similar score |
| Mistral Nemo Instruct 2407Mistral AI | 65.4 | 11 GB | ~81 | similar score |
| Qwen2.5 7B InstructAlibaba | 65.1 | 5.8 GB | ~131 | similar score |
| Gemma 3 4B ITGoogle | 64.0 | 5 GB | ~231 | similar score |
| Llama 3.1 8B InstructMeta | 62.5 | 7.9 GB | ~124 | same size |
| Meta Llama 3 8B InstructMeta | 62.0 | 7.9 GB | ~124 | same size |
| SenseNova Loop 9B Previewsensenova | — | 8.2 GB | ~103 | same size |
Specifications
- Publisher
- Family
- Gemma 2
- Parameters
- 9.2B
- Context Length
- 8,192 tokens
- Release Date
- 2024-06-24
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does Gemma 2 9B IT need?
Gemma 2 9B IT requires 6.1 GB of VRAM at Q4_K_M, or 20.3 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 9.2B × 4.8 bits ÷ 8 = 5.5 GB
KV Cache + Overhead ≈ 0.6 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 2 9B IT?
For Gemma 2 9B IT, Q4_K_M (6.1 GB) offers the best balance of quality and VRAM usage. Q4_K_L (6.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma 2 9B IT on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Gemma 2 9B IT, the smallest at IQ2_S. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 6.1 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run Gemma 2 9B IT locally?
Yes — Gemma 2 9B IT can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 2 9B IT?
At Q4_K_M, Gemma 2 9B IT can reach ~787 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~107 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 ÷ 6.1000 × 0.65 = ~852 tok/s
Estimated speed at Q4_K_M (6.1 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gemma 2 9B IT?
At Q4_K_M, the download is about 5.55 GB. The full-precision BF16 version is 18.48 GB. The smallest option (IQ2_XXS) is 2.54 GB.
Which GPUs can run Gemma 2 9B IT?
52 consumer GPUs can run Gemma 2 9B IT at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Gemma 2 9B IT?
151 devices with unified memory can run Gemma 2 9B IT at Q4_K_M (6.1 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.