Datagemma Rag 27B IT — Hardware Requirements & GPU Compatibility
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Datagemma Rag 27B IT is a 27.2B-parameter open language model from Google in the Gemma family. At BF16 it needs about 59.90 GB of VRAM — see which GPUs and Macs can run it below.
89 downloads
194 likesSpecifications
- Publisher
- Family
- Gemma
- Parameters
- 27.2B
- Release Date
- 2024-08-26
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Datagemma Rag 27B IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 59.9 GB | — | 54.45 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 Datagemma Rag 27B IT?
BF16 · 59.9 GBDatagemma Rag 27B IT (BF16) requires 59.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 78+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Datagemma Rag 27B IT?
BF16 · 59.9 GB22 devices with unified memory can run Datagemma Rag 27B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 96GB).
Runs great
— Plenty of headroom
Related Models
Frequently Asked Questions
- How much VRAM does Datagemma Rag 27B IT need?
Datagemma Rag 27B IT requires 59.9 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 27.2B × 16 bits ÷ 8 = 54.5 GB
KV Cache + Overhead ≈ 5.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1659.9 GB- Can NVIDIA GeForce RTX 5090 run Datagemma Rag 27B IT?
No — Datagemma Rag 27B IT requires at least 59.9 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Datagemma Rag 27B IT on a Mac?
Datagemma Rag 27B IT requires at least 59.9 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 Datagemma Rag 27B IT locally?
Yes — Datagemma Rag 27B IT can run locally on consumer hardware. At BF16 quantization it needs 59.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Datagemma Rag 27B IT?
At BF16, Datagemma Rag 27B IT can reach ~80 tok/s on AMD Instinct MI350X. 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 ÷ 59.9 × 0.65 = ~87 tok/s
Estimated speed at BF16 (59.9 GB)
~87 tok/s~87 tok/s~80 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Datagemma Rag 27B IT?
At BF16, the download is about 54.45 GB.
- Which GPUs can run Datagemma Rag 27B IT?
No single consumer GPU has enough VRAM to run Datagemma Rag 27B IT at BF16 (59.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run Datagemma Rag 27B IT?
23 devices with unified memory can run Datagemma Rag 27B IT at BF16 (59.9 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), 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.