Gemma 2 Mitra E — Hardware Requirements & GPU Compatibility
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179 downloads
3 likesSpecifications
- Publisher
- buddhist-nlp
- Family
- Gemma 2
- Parameters
- 9.2B
- Architecture
- Gemma2ForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 256,002
- Release Date
- 2026-03-07
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HuggingFace
How Much VRAM Does Gemma 2 Mitra E Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 19.4 GB | 21.3 GB | 18.48 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Gemma 2 Mitra E?
BF16 · 19.4 GBGemma 2 Mitra E (BF16) requires 19.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 26+ GB is recommended. Using the full 8K context window can add up to 1.9 GB, bringing total usage to 21.3 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Gemma 2 Mitra E?
BF16 · 19.4 GB21 devices with unified memory can run Gemma 2 Mitra E, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 2 Mitra E need?
Gemma 2 Mitra E requires 19.4 GB of VRAM at BF16. Full 8K context adds up to 1.9 GB (21.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.2B × 16 bits ÷ 8 = 18.5 GB
KV Cache + Overhead ≈ 0.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.8 GB (at full 8K context)
VRAM usage by quantization
BF1619.4 GBBF16 + full context21.3 GB- Can I run Gemma 2 Mitra E on a Mac?
Gemma 2 Mitra E requires at least 19.4 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 2 Mitra E locally?
Yes — Gemma 2 Mitra E can run locally on consumer hardware. At BF16 quantization it needs 19.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 2 Mitra E?
At BF16, Gemma 2 Mitra E can reach ~150 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~34 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 ÷ 19.4 × 0.55 = ~150 tok/s
Estimated speed at BF16 (19.4 GB)
AMD Instinct MI300X~150 tok/sNVIDIA GeForce RTX 4090~34 tok/sNVIDIA H100 SXM~112 tok/sAMD Instinct MI250X~93 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gemma 2 Mitra E?
At BF16, the download is about 18.48 GB.
- Which GPUs can run Gemma 2 Mitra E?
6 consumer GPUs can run Gemma 2 Mitra E at BF16 (19.4 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 2 Mitra E?
21 devices with unified memory can run Gemma 2 Mitra E at BF16 (19.4 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.