T5gemma B B Ul2 IT — Hardware Requirements & GPU Compatibility
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T5gemma B B Ul2 IT is a 591M-parameter open language model from Google in the Gemma family. At BF16 it needs about 1.30 GB of VRAM — see which GPUs and Macs can run it below.
317 downloads
6 likesSpecifications
- Publisher
- Family
- Gemma
- Parameters
- 591M
- Release Date
- 2025-06-19
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does T5gemma B B Ul2 IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 1.3 GB | — | 1.18 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 T5gemma B B Ul2 IT?
BF16 · 1.3 GBT5gemma B B Ul2 IT (BF16) requires 1.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run T5gemma B B Ul2 IT?
BF16 · 1.3 GB59 devices with unified memory can run T5gemma B B Ul2 IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does T5gemma B B Ul2 IT need?
T5gemma B B Ul2 IT requires 1.3 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 591M × 16 bits ÷ 8 = 1.2 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF161.3 GB- Can I run T5gemma B B Ul2 IT on a Mac?
T5gemma B B Ul2 IT requires at least 1.3 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 T5gemma B B Ul2 IT locally?
Yes — T5gemma B B Ul2 IT can run locally on consumer hardware. At BF16 quantization it needs 1.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is T5gemma B B Ul2 IT?
At BF16, T5gemma B B Ul2 IT can reach ~3385 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~504 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 ÷ 1.3 × 0.65 = ~4000 tok/s
Estimated speed at BF16 (1.3 GB)
~4000 tok/s~504 tok/s~4000 tok/s~3385 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of T5gemma B B Ul2 IT?
At BF16, the download is about 1.18 GB.
- Which GPUs can run T5gemma B B Ul2 IT?
50 consumer GPUs can run T5gemma B B Ul2 IT at BF16 (1.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run T5gemma B B Ul2 IT?
59 devices with unified memory can run T5gemma B B Ul2 IT at BF16 (1.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.