T5gemma 2B 2B Ul2 — Hardware Requirements & GPU Compatibility
Chat
T5gemma 2B 2B Ul2 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.
10.1K downloads
25 likesSpecifications
- Publisher
- Family
- Gemma 2
- Parameters
- 5.6B
- Release Date
- 2025-07-09
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does T5gemma 2B 2B Ul2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.6 GB | — | 2.38 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.7 GB | — | 2.45 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 3 GB | — | 2.73 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 3.1 GB | — | 2.80 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 3.7 GB | — | 3.36 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 4.4 GB | — | 3.99 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 5.1 GB | — | 4.62 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 6.2 GB | — | 5.60 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run T5gemma 2B 2B Ul2?
Q4_K_M · 3.7 GBT5gemma 2B 2B Ul2 (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. 35 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?
Q4_K_M · 3.7 GB33 devices with unified memory can run T5gemma 2B 2B Ul2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does T5gemma 2B 2B Ul2 need?
T5gemma 2B 2B Ul2 requires 3.7 GB of VRAM at Q4_K_M, or 6.2 GB at Q8_0.
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)
VRAM usage by quantization
Q4_K_M3.7 GB- What's the best quantization for T5gemma 2B 2B Ul2?
For T5gemma 2B 2B Ul2, Q4_K_M (3.7 GB) offers the best balance of quality and VRAM usage. Q5_K_S (4.2 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_03.1 GBQ4_K_S3.5 GBQ4_K_M ★3.7 GBQ5_K_S4.2 GBQ8_06.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run T5gemma 2B 2B Ul2 on a Mac?
T5gemma 2B 2B Ul2 requires at least 2.6 GB at Q2_K, 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 2B 2B Ul2 locally?
Yes — T5gemma 2B 2B Ul2 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?
At Q4_K_M, T5gemma 2B 2B Ul2 can reach ~790 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 3.7 × 0.55 = ~790 tok/s
Estimated speed at Q4_K_M (3.7 GB)
~790 tok/s~178 tok/s~591 tok/s~488 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?
At Q4_K_M, the download is about 3.36 GB. The full-precision Q8_0 version is 5.60 GB. The smallest option (Q2_K) is 2.38 GB.
- Which GPUs can run T5gemma 2B 2B Ul2?
35 consumer GPUs can run T5gemma 2B 2B Ul2 at Q4_K_M (3.7 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 T5gemma 2B 2B Ul2?
33 devices with unified memory can run T5gemma 2B 2B Ul2 at Q4_K_M (3.7 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.