T5gemma 2B 2B Ul2 IT — Hardware Requirements & GPU Compatibility
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T5gemma 2B 2B Ul2 IT is a 5.6B-parameter open language model from Google in the Gemma 2 family. At Q4_K_M it needs about 3.69 GB of VRAM — see which GPUs and Macs can run it below.
47.8K downloads
10 likesSpecifications
- Publisher
- Family
- Gemma 2
- Parameters
- 5.6B
- Release Date
- 2025-06-19
- License
- Gemma Terms
Get Started
HuggingFace
Run in cloud
Fits on RTX 3060 12GB (8 GB headroom) · Q4_K_M
- Generation speed
- ~63 tok/s
- generation speed
- Cost per 1M output tokens
- $0.27
- per 1M output tokens
How Much VRAM Does T5gemma 2B 2B Ul2 IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 2.6 GB | — | 2.38 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 3 GB | — | 2.73 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 3.7 GB | — | 3.36 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 4.4 GB | — | 3.99 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 5.1 GB | — | 4.62 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 6.2 GB | — | 5.60 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 12.3 GB | — | 11.19 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 2B 2B Ul2 IT?
Q4_K_M · 3.7 GBT5gemma 2B 2B Ul2 IT (Q4_K_M) requires 3.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run T5gemma 2B 2B Ul2 IT?
Q4_K_M · 3.7 GB59 devices with unified memory can run T5gemma 2B 2B Ul2 IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does T5gemma 2B 2B Ul2 IT need?
T5gemma 2B 2B Ul2 IT requires 3.7 GB of VRAM at Q4_K_M, or 12.3 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 5.6B × 4.8 bits ÷ 8 = 3.4 GB
KV Cache + Overhead ≈ 0.3 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
Q4_K_M3.7 GB- What's the best quantization for T5gemma 2B 2B Ul2 IT?
For T5gemma 2B 2B Ul2 IT, Q4_K_M (3.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (4.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.6 GB.
VRAM requirement by quantization
Q2_K2.6 GBQ4_K_M ★3.7 GBQ5_K_M4.4 GBQ6_K5.1 GBQ8_06.2 GBBF1612.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run T5gemma 2B 2B Ul2 IT on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 38 other Macs can run T5gemma 2B 2B Ul2 IT. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 3.7 GB of usable unified memory (RAM minus macOS overhead).
- Can I run T5gemma 2B 2B Ul2 IT locally?
Yes — T5gemma 2B 2B Ul2 IT can run locally on consumer hardware. At Q4_K_M quantization it needs 3.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is T5gemma 2B 2B Ul2 IT?
At Q4_K_M, T5gemma 2B 2B Ul2 IT can reach ~1301 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~178 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 ÷ 3.69 × 0.65 = ~1409 tok/s
Estimated speed at Q4_K_M (3.7 GB)
~1409 tok/s~178 tok/s~1409 tok/s~1301 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of T5gemma 2B 2B Ul2 IT?
At Q4_K_M, the download is about 3.36 GB. The full-precision BF16 version is 11.19 GB. The smallest option (Q2_K) is 2.38 GB.
- Which GPUs can run T5gemma 2B 2B Ul2 IT?
52 consumer GPUs can run T5gemma 2B 2B Ul2 IT at Q4_K_M (3.7 GB). 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 T5gemma 2B 2B Ul2 IT?
59 devices with unified memory can run T5gemma 2B 2B Ul2 IT at Q4_K_M (3.7 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.