FitMyLLM
Snowflake/Dense

SSnowflake Arctic Embed M v2.0

- 12/11/2024: Release of Technical Report - 12/04/2024: Release of snowflake-arctic-embed-l-v2.0 and snowflake-arctic-embed-m-v2.0 our newest models with multilingual workloads in mind.

embeddingmultilingual
0.305B
Parameters
8K
Context length
0
Benchmarks
6
Quantizations
0
Architecture
Dense
Released
2024-12-04
Layers
12
KV Heads
12
Head Dim
64
Family
embedding

Quantization Options

Context length:
QuantBitsVRAM @ 8KQuality
Q4_K_M4.89
0.8 GB
0.7 + 0.1 KV
good
Q5_K_S5.57
0.8 GB
0.7 + 0.1 KV
good
Q5_K_M5.7
0.8 GB
0.7 + 0.1 KV
good
Q6_K6.56
0.9 GB
0.7 + 0.1 KV
excellent
Q8_08.5
1.0 GB
0.8 + 0.1 KV
lossless
FP1616
1.2 GB
1.1 + 0.1 KV
lossless

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

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Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run embedding:0.305b-q4_K_M

Tag may need adjustment — check ollama.com/library/embedding 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.

Find the best GPU for Snowflake Arctic Embed M v2.0

Build Hardware for Snowflake Arctic Embed M v2.0
▸ SPEC SHEET

Snowflake Arctic Embed M v2.00.305B Dense.

▸ SPECIFICATIONS
PARAMETERS
0.305B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
8K tokens
CAPABILITIES
embedding, multilingual
RELEASE DATE
2024-12-04
PROVIDER
Snowflake
FAMILY
embedding
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.890.7 GB94%
Q5_K_S5.570.7 GB96%
Q5_K_M5.70.7 GB96%
Q6_K6.560.7 GB97%
Q8_08.50.8 GB100%
FP16161.1 GB100%
§ 03COMPATIBLE GPUs
30 @ Q4_K_M