Shieldgemma 2B — Hardware Requirements & GPU Compatibility
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Shieldgemma 2B is a 2.6B-parameter open language model from Google in the Gemma 2 family. At Q4_K_M it needs about 1.73 GB of VRAM — see which GPUs and Macs can run it below.
4.6K downloads
122 likesSpecifications
- Publisher
- Family
- Gemma 2
- Parameters
- 2.6B
- Release Date
- 2024-08-28
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Shieldgemma 2B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.2 GB | — | 1.11 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.3 GB | — | 1.14 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.4 GB | — | 1.27 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.4 GB | — | 1.31 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.7 GB | — | 1.57 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 2.0 GB | — | 1.86 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 2.4 GB | — | 2.16 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 2.9 GB | — | 2.61 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Shieldgemma 2B?
Q4_K_M · 1.7 GBShieldgemma 2B (Q4_K_M) requires 1.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Shieldgemma 2B?
Q4_K_M · 1.7 GB33 devices with unified memory can run Shieldgemma 2B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Shieldgemma 2B need?
Shieldgemma 2B requires 1.7 GB of VRAM at Q4_K_M, or 2.9 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 2.6B × 4.8 bits ÷ 8 = 1.6 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.7 GB- What's the best quantization for Shieldgemma 2B?
For Shieldgemma 2B, Q4_K_M (1.7 GB) offers the best balance of quality and VRAM usage. Q5_K_S (2.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.2 GB.
VRAM requirement by quantization
Q2_K1.2 GBQ4_01.4 GBQ4_K_S1.6 GBQ4_K_M ★1.7 GBQ5_K_S2.0 GBQ8_02.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Shieldgemma 2B on a Mac?
Shieldgemma 2B requires at least 1.2 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 Shieldgemma 2B locally?
Yes — Shieldgemma 2B can run locally on consumer hardware. At Q4_K_M quantization it needs 1.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Shieldgemma 2B?
At Q4_K_M, Shieldgemma 2B can reach ~1685 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~379 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 ÷ 1.7 × 0.55 = ~1685 tok/s
Estimated speed at Q4_K_M (1.7 GB)
~1685 tok/s~379 tok/s~1259 tok/s~1042 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Shieldgemma 2B?
At Q4_K_M, the download is about 1.57 GB. The full-precision Q8_0 version is 2.61 GB. The smallest option (Q2_K) is 1.11 GB.
- Which GPUs can run Shieldgemma 2B?
35 consumer GPUs can run Shieldgemma 2B at Q4_K_M (1.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Shieldgemma 2B?
33 devices with unified memory can run Shieldgemma 2B at Q4_K_M (1.7 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.