FitMyLLM
Google/Dense

GoogleTranslateGemma 27B

TranslateGemma 27B — most capable translation model from Google.

chatmultilingualvision
28.84B
Parameters
128K
Context length
7
Benchmarks
10
Quantizations
7K
HF downloads
Architecture
Dense
Released
2026-01-13
Layers
62
KV Heads
16
Head Dim
128
Family
gemma

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q3_K_M4
20.7 GB
14.9 + 5.8 KV
low
Q3_K_L4.3
21.8 GB
16.0 + 5.8 KV
moderate
IQ4_XS4.46
22.4 GB
16.6 + 5.8 KV
moderate
Q4_K_S4.67
23.1 GB
17.3 + 5.8 KV
moderate
Q4_K_M4.89
23.9 GB
18.1 + 5.8 KV
good
Q5_K_S5.57
26.4 GB
20.6 + 5.8 KV
good
Q5_K_M5.7
26.8 GB
21.0 + 5.8 KV
good
Q6_K6.56
29.9 GB
24.1 + 5.8 KV
excellent
Q8_08.5
36.9 GB
31.1 + 5.8 KV
lossless
FP1616
64.0 GB
58.2 + 5.8 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN TRANSLATEGEMMA 27B NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

Loading ratings...

Benchmarks (7)

IFEval75.5
BBH51.1
BigCodeBench42.8
MMLU-PRO40.3
MATH27.9
MUSR16.9
GPQA16.0

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run translategemma:27b-it-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

NVIDIA RTX A4500
20 GB VRAM • 640 GB/s
NVIDIA
$2000
Apple M4 Pro (24GB)
24 GB VRAM • 273 GB/s
APPLE
$1399
NVIDIA L4 24GB
24 GB VRAM • 300 GB/s
NVIDIA
$2500
Apple M2 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M3 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M4 (24GB)
24 GB VRAM • 120 GB/s
APPLE
$699
NVIDIA Tesla M40 24 GB
24 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla P10
24 GB VRAM • 694 GB/s
NVIDIA
NVIDIA Tesla P40
24 GB VRAM • 347 GB/s
NVIDIA
NVIDIA RTX A5000
24 GB VRAM • 768 GB/s
NVIDIA
$2500
NVIDIA L40 CNX
24 GB VRAM • 864 GB/s
NVIDIA
$5000

Find the best GPU for TranslateGemma 27B

Build Hardware for TranslateGemma 27B

TranslateGemma 27B — most capable translation model from Google.

▸ SPEC SHEET

TranslateGemma 27B28.84B Dense.

▸ SPECIFICATIONS
PARAMETERS
28.84B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
128K tokens
CAPABILITIES
chat, multilingual, vision
RELEASE DATE
2026-01-13
PROVIDER
Google
FAMILY
gemma
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q3_K_M414.9 GB88%
Q3_K_L4.316.0 GB90%
IQ4_XS4.4616.6 GB92%
Q4_K_S4.6717.3 GB93%
Q4_K_M4.8918.1 GB94%
Q5_K_S5.5720.6 GB96%
Q5_K_M5.721.0 GB96%
Q6_K6.5624.1 GB97%
Q8_08.531.1 GB100%
FP161658.2 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO40.3
MATH27.9
IFEval75.5
BBH51.1
GPQA16.0
MUSR16.9
BigCodeBench42.8
§ 02RUN COMMAND

Run TranslateGemma 27B locally with Ollama — needs 18.1 GB VRAM at Q4_K_M:

$ollama run translategemma:27b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M