Translategemma 4B IT — Hardware Requirements & GPU Compatibility
Vision
TranslateGemma 4B IT is Google's roughly 5-billion-parameter instruction-tuned model for translation, built on the Gemma 3 vision-language architecture and accepting text and images. As the name indicates, it targets machine translation between languages rather than general chat. The listed size includes the vision components. At this size it fits comfortably on a single modest consumer GPU once quantized, and can run on a laptop. It is distributed under the Gemma Terms of Use, and access requires acknowledging the license on Hugging Face. Published in January 2026, it is part of Google's Gemma family of open models and descends from the Gemma 3 line.
38.9K downloads
856 likesSpecifications
- Publisher
- Family
- Gemma 4
- Parameters
- 5.0B
- Release Date
- 2026-01-12
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Translategemma 4B IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.3 GB | — | 2.11 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.4 GB | — | 2.17 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.7 GB | — | 2.42 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.7 GB | — | 2.49 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 3.3 GB | — | 2.98 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.9 GB | — | 3.54 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 4.5 GB | — | 4.10 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 5.5 GB | — | 4.97 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Translategemma 4B IT?
Q4_K_M · 3.3 GBTranslategemma 4B IT (Q4_K_M) requires 3.3 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 Translategemma 4B IT?
Q4_K_M · 3.3 GB59 devices with unified memory can run Translategemma 4B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Translategemma 4B IT
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Translategemma 4B IT need?
Translategemma 4B IT requires 3.3 GB of VRAM at Q4_K_M, or 10.9 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 5.0B × 4.8 bits ÷ 8 = 3 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M3.3 GB- What's the best quantization for Translategemma 4B IT?
For Translategemma 4B IT, Q4_K_M (3.3 GB) offers the best balance of quality and VRAM usage. Q5_0 (3.4 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 2.3 GB.
VRAM requirement by quantization
IQ3_XS2.3 GBQ3_K_M2.7 GBIQ4_NL3.1 GBQ4_K_M ★3.3 GBQ5_K_S3.8 GBBF1610.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Translategemma 4B IT on a Mac?
Translategemma 4B IT requires at least 2.3 GB at IQ3_XS, 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 Translategemma 4B IT locally?
Yes — Translategemma 4B IT can run locally on consumer hardware. At Q4_K_M quantization it needs 3.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Translategemma 4B IT?
At Q4_K_M, Translategemma 4B IT can reach ~1463 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~200 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 ÷ 3.3 × 0.65 = ~1585 tok/s
Estimated speed at Q4_K_M (3.3 GB)
~1585 tok/s~200 tok/s~1585 tok/s~1463 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Translategemma 4B IT?
At Q4_K_M, the download is about 2.98 GB. The full-precision BF16 version is 9.94 GB. The smallest option (IQ3_XS) is 2.05 GB.
- Which GPUs can run Translategemma 4B IT?
52 consumer GPUs can run Translategemma 4B IT at Q4_K_M (3.3 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 Translategemma 4B IT?
59 devices with unified memory can run Translategemma 4B IT at Q4_K_M (3.3 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.