Gemma 2 9B IT — Hardware Requirements & GPU Compatibility
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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.
239.7K downloads
779 likesSpecifications
- Publisher
- Family
- Gemma 2
- Parameters
- 9.2B
- Context Length
- 8,192 tokens
- Release Date
- 2024-08-27
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Gemma 2 9B IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XS | 2.40 | 3.0 GB | — | 2.77 GB | Importance-weighted 2-bit, extra small |
| IQ2_S | 2.50 | 3.2 GB | — | 2.89 GB | Importance-weighted 2-bit, small |
| IQ2_M | 2.70 | 3.4 GB | — | 3.12 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 3.9 GB | — | 3.58 GB | Importance-weighted 3-bit |
| IQ3_XS | 3.30 | 4.2 GB | — | 3.81 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 4.3 GB | — | 3.93 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.5 GB | — | 4.04 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 4.6 GB | — | 4.16 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 5.0 GB | — | 4.51 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 5.2 GB | — | 4.74 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 5.5 GB | — | 4.97 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 5.7 GB | — | 5.20 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 6.1 GB | — | 5.55 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 6.2 GB | — | 5.66 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 7.0 GB | — | 6.35 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 7.2 GB | — | 6.58 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 7.4 GB | — | 6.70 GB | 5-bit large quantization |
| Q6_K | 6.60 | 8.4 GB | — | 7.62 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 10.2 GB | — | 9.24 GB | 8-bit quantization, near-lossless |
Which GPUs 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. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Gemma 2 9B IT?
Q4_K_M · 6.1 GB33 devices with unified memory can run Gemma 2 9B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Derivatives (2)
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 10.2 GB at Q8_0.
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)
VRAM usage by quantization
Q4_K_M6.1 GB- 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_XS at 3.0 GB.
VRAM requirement by quantization
IQ2_XS3.0 GB~57%Q2_K4.3 GB~75%Q3_K_L5.2 GB~86%Q4_K_M ★6.1 GB~89%Q5_K_S7.0 GB~92%Q8_010.2 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 2 9B IT on a Mac?
Gemma 2 9B IT requires at least 3.0 GB at IQ2_XS, 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 2 9B IT locally?
Yes — Gemma 2 9B IT can run locally on consumer hardware. At Q4_K_M quantization it needs 6.1 GB of VRAM. 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 ~478 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 6.1 × 0.55 = ~478 tok/s
Estimated speed at Q4_K_M (6.1 GB)
AMD Instinct MI300X~478 tok/sNVIDIA GeForce RTX 4090~107 tok/sNVIDIA H100 SXM~357 tok/sAMD Instinct MI250X~295 tok/sReal-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 Q8_0 version is 9.24 GB. The smallest option (IQ2_XS) is 2.77 GB.