Gemma 2 9B — Hardware Requirements & GPU Compatibility
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Gemma 2 9B is a 9.2B-parameter open language model from Google in the Gemma 2 family. At Q4_K_M it needs about 6.10 GB of VRAM — see which GPUs and Macs can run it below.
75.4K downloads
709 likesSpecifications
- Publisher
- Family
- Gemma 2
- Parameters
- 9.2B
- Release Date
- 2024-06-24
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Gemma 2 9B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| 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 |
| Q3_K_M | 3.90 | 5.0 GB | — | 4.51 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 5.1 GB | — | 4.62 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 6.1 GB | — | 5.55 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 7.2 GB | — | 6.58 GB | 5-bit medium quantization — good quality/size tradeoff |
| 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 |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Gemma 2 9B?
Q4_K_M · 6.1 GBGemma 2 9B (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. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Gemma 2 9B?
Q4_K_M · 6.1 GB58 devices with unified memory can run Gemma 2 9B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Gemma 2 9B
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 2 9B need?
Gemma 2 9B 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)
VRAM usage by quantization
Q4_K_M6.1 GB- What's the best quantization for Gemma 2 9B?
For Gemma 2 9B, Q4_K_M (6.1 GB) offers the best balance of quality and VRAM usage. Q5_0 (6.3 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 4.2 GB.
VRAM requirement by quantization
IQ3_XS4.2 GBQ3_K_M5.0 GBQ4_15.7 GBQ4_K_M ★6.1 GBQ5_K_S7.0 GBBF1620.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 2 9B on a Mac?
Gemma 2 9B requires at least 4.2 GB at IQ3_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 locally?
Yes — Gemma 2 9B 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?
At Q4_K_M, Gemma 2 9B can reach ~721 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 ÷ 6.1 × 0.65 = ~853 tok/s
Estimated speed at Q4_K_M (6.1 GB)
~853 tok/s~107 tok/s~853 tok/s~721 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?
At Q4_K_M, the download is about 5.55 GB. The full-precision BF16 version is 18.48 GB. The smallest option (IQ3_XS) is 3.81 GB.
- Which GPUs can run Gemma 2 9B?
50 consumer GPUs can run Gemma 2 9B at Q4_K_M (6.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 2 9B?
59 devices with unified memory can run Gemma 2 9B at Q4_K_M (6.1 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.