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 While superseded by Gemma 2 and Gemma 3 in terms of benchmark performance, the original Gemma 7B remains a solid foundation model and a useful reference point in the evolution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.
28.1K downloads
3.4K likesSpecifications
- Publisher
- Family
- Gemma
- Parameters
- 8.5B
- Release Date
- 2024-02-08
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Gemma 7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 18.8 GB | — | 17.08 GB | Brain floating point 16 — preferred for training |
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?
BF16 · 18.8 GBGemma 7B (BF16) requires 18.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 25+ GB is recommended. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Gemma 7B?
BF16 · 18.8 GB41 devices with unified memory can run Gemma 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightBenchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Gemma 7B need?
Gemma 7B requires 18.8 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8.5B × 16 bits ÷ 8 = 17.1 GB
KV Cache + Overhead ≈ 1.7 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1618.8 GB- Can I run Gemma 7B on a Mac?
Gemma 7B requires at least 18.8 GB at BF16, 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 BF16 quantization it needs 18.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 7B?
At BF16, Gemma 7B can reach ~256 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~35 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 ÷ 18.8 × 0.65 = ~277 tok/s
Estimated speed at BF16 (18.8 GB)
~277 tok/s~35 tok/s~277 tok/s~256 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 BF16, the download is about 17.08 GB.
- Which GPUs can run Gemma 7B?
8 consumer GPUs can run Gemma 7B at BF16 (18.8 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Gemma 7B?
41 devices with unified memory can run Gemma 7B at BF16 (18.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.