Gemma 7B — Hardware Requirements & GPU Compatibility
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Google Gemma 7B is a 7-billion parameter base (pretrained) model from the original Gemma generation, Google's first openly available family of language models. It represents Google's initial entry into the open-weight LLM space. While superseded by Gemma 2 and Gemma 3 in terms of benchmark performance, the original Gemma 7B remains a solid foundation model and a useful reference point in the evolution of Google's open models. Released under the Gemma license.
26.2K downloads
3.4K likesSpecifications
- Publisher
- Family
- Gemma
- Parameters
- 8.5B
- Release Date
- 2024-02-08
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Gemma 7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.0 GB | — | 3.63 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.1 GB | — | 3.74 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.6 GB | — | 4.16 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.7 GB | — | 4.27 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.6 GB | — | 5.12 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.7 GB | — | 6.08 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.8 GB | — | 7.04 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 9.4 GB | — | 8.54 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 7B?
Q4_K_M · 5.6 GBGemma 7B (Q4_K_M) requires 5.6 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 7B?
Q4_K_M · 5.6 GB58 devices with unified memory can run Gemma 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Gemma 7B
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 7B need?
Gemma 7B requires 5.6 GB of VRAM at Q4_K_M, or 18.8 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8.5B × 4.8 bits ÷ 8 = 5.1 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M5.6 GB- What's the best quantization for Gemma 7B?
For Gemma 7B, Q4_K_M (5.6 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.0 GB.
VRAM requirement by quantization
Q2_K4.0 GBQ4_04.7 GBQ4_K_S5.3 GBQ4_K_M ★5.6 GBQ5_K_M6.7 GBBF1618.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 7B on a Mac?
Gemma 7B requires at least 4.0 GB at Q2_K, 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 7B locally?
Yes — Gemma 7B can run locally on consumer hardware. At Q4_K_M quantization it needs 5.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 7B?
At Q4_K_M, Gemma 7B can reach ~782 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~116 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 ÷ 5.6 × 0.65 = ~924 tok/s
Estimated speed at Q4_K_M (5.6 GB)
~924 tok/s~116 tok/s~924 tok/s~782 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gemma 7B?
At Q4_K_M, the download is about 5.12 GB. The full-precision BF16 version is 17.08 GB. The smallest option (Q2_K) is 3.63 GB.
- Which GPUs can run Gemma 7B?
50 consumer GPUs can run Gemma 7B at Q4_K_M (5.6 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 7B?
59 devices with unified memory can run Gemma 7B at Q4_K_M (5.6 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.