Gemma 3B Hukuk Merged 16bit — Hardware Requirements & GPU Compatibility
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Gemma 3B Hukuk Merged 16bit is a 1.0B-parameter open language model from sinanelms in the Gemma family. It supports a context window of up to 32,768 tokens. At BF16 it needs about 2.40 GB of VRAM — see which GPUs and Macs can run it below.
10 downloads
2 likesSpecifications
- Publisher
- sinanelms
- Family
- Gemma
- Parameters
- 1.0B
- Architecture
- Gemma3ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2025-06-25
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Gemma 3B Hukuk Merged 16bit Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 2.4 GB | 3.3 GB | 2.04 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Gemma 3B Hukuk Merged 16bit?
BF16 · 2.4 GBGemma 3B Hukuk Merged 16bit (BF16) requires 2.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 33K context window can add up to 0.9 GB, bringing total usage to 3.3 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Gemma 3B Hukuk Merged 16bit?
BF16 · 2.4 GB33 devices with unified memory can run Gemma 3B Hukuk Merged 16bit, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 3B Hukuk Merged 16bit need?
Gemma 3B Hukuk Merged 16bit requires 2.4 GB of VRAM at BF16. Full 33K context adds up to 0.9 GB (3.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.0B × 16 bits ÷ 8 = 2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.3 GB (at full 33K context)
VRAM usage by quantization
BF162.4 GBBF16 + full context3.3 GB- Can I run Gemma 3B Hukuk Merged 16bit on a Mac?
Gemma 3B Hukuk Merged 16bit requires at least 2.4 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 3B Hukuk Merged 16bit locally?
Yes — Gemma 3B Hukuk Merged 16bit can run locally on consumer hardware. At BF16 quantization it needs 2.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 3B Hukuk Merged 16bit?
At BF16, Gemma 3B Hukuk Merged 16bit can reach ~1215 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~273 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 ÷ 2.4 × 0.55 = ~1215 tok/s
Estimated speed at BF16 (2.4 GB)
~1215 tok/s~273 tok/s~908 tok/s~751 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gemma 3B Hukuk Merged 16bit?
At BF16, the download is about 2.04 GB.
- Which GPUs can run Gemma 3B Hukuk Merged 16bit?
35 consumer GPUs can run Gemma 3B Hukuk Merged 16bit at BF16 (2.4 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 Gemma 3B Hukuk Merged 16bit?
33 devices with unified memory can run Gemma 3B Hukuk Merged 16bit at BF16 (2.4 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.