FitMyLLM
Google/Dense

GoogleRecurrentGemma 9B

Griffin architecture — fixed-state recurrence, not attention. Constant RAM usage.

chat
9.63B
Parameters
8K
Context length
7
Benchmarks
6
Quantizations
Architecture
Dense
Released
2024-04-01
Layers
42
KV Heads
1
Head Dim
256
Family
gemma

Quantization Options

Context length:
QuantBitsVRAM @ 8KQuality
Q4_K_M4.89
6.5 GB
6.4 + 0.2 KV
good
Q5_K_S5.57
7.4 GB
7.2 + 0.2 KV
good
Q5_K_M5.7
7.5 GB
7.3 + 0.2 KV
good
Q6_K6.56
8.5 GB
8.4 + 0.2 KV
excellent
Q8_08.5
10.9 GB
10.7 + 0.2 KV
lossless
FP1616
19.9 GB
19.7 + 0.2 KV
lossless

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

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Community Ratings

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Benchmarks (7)

IFEval80.5
BBH44.2
MMLU-PRO37.4
BigCodeBench34.7
MATH23.3
GPQA12.8
MUSR12.2

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run gemma:10b-q4_K_M

Tag may need adjustment — check ollama.com/library/gemma for available tags.

▸ 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.

Apple M1 (8GB)
8 GB VRAM • 68 GB/s
APPLE
$499
Apple M2 (8GB)
8 GB VRAM • 100 GB/s
APPLE
$599
Apple M3 (8GB)
8 GB VRAM • 100 GB/s
APPLE
$599
NVIDIA Tesla K8
8 GB VRAM • 160 GB/s
NVIDIA
NVIDIA Tesla M60
8 GB VRAM • 160 GB/s
NVIDIA

Find the best GPU for RecurrentGemma 9B

Build Hardware for RecurrentGemma 9B
▸ SPEC SHEET

RecurrentGemma 9B9.63B Dense.

▸ SPECIFICATIONS
PARAMETERS
9.63B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
8K tokens
CAPABILITIES
chat
RELEASE DATE
2024-04-01
PROVIDER
Google
FAMILY
gemma
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.896.4 GB94%
Q5_K_S5.577.2 GB96%
Q5_K_M5.77.3 GB96%
Q6_K6.568.4 GB97%
Q8_08.510.7 GB100%
FP161619.7 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO37.4
MATH23.3
IFEval80.5
BBH44.2
GPQA12.8
MUSR12.2
BigCodeBench34.7
§ 02RUN COMMAND

Run RecurrentGemma 9B locally with Ollama — needs 6.4 GB VRAM at Q4_K_M:

$ollama run recurrentgemma:9b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M