T5gemma 2B 2B Ul2 — Hardware Requirements & GPU Compatibility
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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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~178 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~19 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run T5gemma 2B 2B Ul2
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve google/t5gemma-2b-2b-ul2 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (6 GB headroom) · Q4_K_M
- Generation speed
- ~63 tok/s
- generation speed
- Cost per 1M output tokens
- $0.42
- per 1M output tokens
How Much VRAM Does T5gemma 2B 2B Ul2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.6 GB | 7.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~250 tok/s | |
| 3 GB | 8 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~218 tok/s | |
| 3.7 GB | 8.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~178 tok/s | |
| 4.4 GB | 9.4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~149 tok/s | |
| 5.1 GB | 10.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~129 tok/s | |
| 6.2 GB | 11.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~106 tok/s | |
| 12.3 GB | 17.3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~53 tok/s |
Selected Q4_K_M
4-bit medium quantization6.0GBat 16K context
- Weights
- 3.1 GB
- KV cache
- 2.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 18.0 GB free · ~178 tok/s
≥50 tok/s needs ≥284 GB/s · RTX 5090 ~316, RTX 3090 Ti ~178
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run T5gemma 2B 2B Ul2, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 2.0 GB free · up to 16K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~134Simulate output on RTX 3080RTX 2080 Ti~109Simulate output on RTX 2080 TiRTX 3070 Ti~107Simulate output on RTX 3070 TiM3*8 GB~19Simulate output on iMac 24" M3 (8 GB)Orin Nano~18Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~13Simulate output on iPhone 1720 GB
fits · 14.0 GB free · up to 32K ctx
1 GPU
RX 7900 XT~130Simulate output on RX 7900 XT32–40 GB
fits · 26.0 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~316Simulate output on RTX 5090M5 Max36 GB~87Simulate output on Mac Studio M5 Max (36 GB)M532 GB~29Simulate output on MacBook Air 13" M5 (32 GB)Orin~36Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~24Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to T5gemma 2B 2B Ul2
Gemma 2 family · 20 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Youtu Parsing OmniTencent | — | 12.7 GB | ~59 | same size |
| Qwen2.5 Omni 3BAlibaba | — | 6 GB | ~180 | same size |
| Chatglm2 6BZ.ai | — | 6.3 GB | ~166 | same size |
| TLive Omni 4BTaoLiveAIGC | — | 13.3 GB | ~54 | same size |
| GPT J 6BEleutherAI | — | 4 GB | ~166 | same size |
| Ko PlatYi 6Bkyujinpy | — | 4 GB | ~163 | same size |
Fine-tunes & variants(2)
Specifications
- Publisher
- Family
- Gemma 2
- Parameters
- 5.6B
- Release Date
- 2025-06-19
- License
- Gemma Terms
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 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
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_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
★ Recommended — best balance of quality and VRAM usage.
Can I run T5gemma 2B 2B Ul2 on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run T5gemma 2B 2B Ul2. 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 locally?
Yes — T5gemma 2B 2B Ul2 can run locally on consumer hardware. 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 ~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)
Real-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 BF16 version is 11.19 GB. The smallest option (Q2_K) is 2.38 GB.
Which GPUs can run T5gemma 2B 2B Ul2?
52 consumer GPUs can run T5gemma 2B 2B Ul2 at Q4_K_M. 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?
163 devices with unified memory can run T5gemma 2B 2B Ul2 at Q4_K_M (3.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, 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.