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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~379 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~41 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Shieldgemma 2B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run shieldgemma:2bQ4_K_M fits fully on the RTX 4090 · 20.4 GB free · 16K context
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (8 GB headroom) · Q4_K_M
- Generation speed
- ~135 tok/s
- generation speed
- Cost per 1M output tokens
- $0.17
- per 1M output tokens
How Much VRAM Does Shieldgemma 2B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.2 GB | 5.2 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~537 tok/s | |
| 1.4 GB | 5.4 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~468 tok/s | |
| 1.7 GB | 5.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~379 tok/s | |
| 2.0 GB | 6.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~320 tok/s | |
| 2.4 GB | 6.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~277 tok/s | |
| 2.9 GB | 6.9 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~228 tok/s | |
| 5.8 GB | 9.8 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~114 tok/s |
Selected Q4_K_M
4-bit medium quantization3.6GBat 16K context
- Weights
- 1.2 GB
- KV cache
- 2.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.4 GB free · ~379 tok/s
≥50 tok/s needs ≥133 GB/s · RTX 5090 ~673, RTX 3090 Ti ~379
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Shieldgemma 2B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.4 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~286Simulate output on RTX 3080RTX 2080 Ti~231Simulate output on RTX 2080 TiRTX 3070 Ti~229Simulate output on RTX 3070 TiM3*8 GB~41Simulate output on iMac 24" M3 (8 GB)Orin Nano~38Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~28Simulate output on iPhone 1720 GB
fits · 16.4 GB free · up to 32K ctx
1 GPU
RX 7900 XT~278Simulate output on RX 7900 XT32–40 GB
fits · 28.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~673Simulate output on RTX 5090M5 Max36 GB~186Simulate output on Mac Studio M5 Max (36 GB)M532 GB~62Simulate output on MacBook Air 13" M5 (32 GB)Orin~77Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~51Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Shieldgemma 2B
Gemma 2 family · 20 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM5 2BOpenBMB | — | 2.5 GB | ~345 | same size |
| North Micro Vision InstructCohere | — | 7.1 GB | ~119 | same size |
| LFM2.5 2.6BLiquid AI | — | 2.3 GB | ~321 | same size |
| Granite 3.1 2B InstructIBM | — | 3.2 GB | ~329 | same size |
| Phi 2Microsoft | — | 2.6 GB | ~248 | same size |
| Ouro 2.6B ThinkingByteDance | — | 12.1 GB | ~102 | same size |
Specifications
- Publisher
- Family
- Gemma 2
- Parameters
- 2.6B
- Release Date
- 2024-07-16
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does Shieldgemma 2B need?
Shieldgemma 2B requires 1.7 GB of VRAM at Q4_K_M, or 5.8 GB at BF16.
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
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_M (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
★ Recommended — best balance of quality and VRAM usage.
Can I run Shieldgemma 2B on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Shieldgemma 2B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run Shieldgemma 2B locally?
Yes — Shieldgemma 2B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Shieldgemma 2B?
At Q4_K_M, Shieldgemma 2B can reach ~2775 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 1.73 × 0.65 = ~3006 tok/s
Estimated speed at Q4_K_M (1.7 GB)
Real-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 BF16 version is 5.23 GB. The smallest option (Q2_K) is 1.11 GB.
Which GPUs can run Shieldgemma 2B?
52 consumer GPUs can run Shieldgemma 2B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Shieldgemma 2B?
163 devices with unified memory can run Shieldgemma 2B at Q4_K_M (1.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.