multilingual-e5-small: Self-Hosting & Deployment Guide
multilingual-e5-small is an open-weight embedding model that turns text into vectors for search and retrieval.
What is multilingual-e5-small?
multilingual-e5-small is an open-weight embedding model that turns text into vectors for search and retrieval, published by Microsoft (intfloat) with open weights at intfloat/multilingual-e5-small. LLM API does not route this model today, so there is no endpoint or code sample for it here. What this page does give you is the self-hosting picture: published weight sizes, the VRAM each quantisation needs and the commands to serve it yourself. Every figure is read from the public repository and refreshed weekly. At import it had 12,188,618 downloads on Hugging Face in the previous 30 days, under the mit licence.
How to run multilingual-e5-small yourself
Open weights (mit), 118M parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.
multilingual-e5-small VRAM requirements
| Precision / quantization | Weights | Figure | Source |
|---|---|---|---|
| BF16 (full precision) | 0.24 GB | Estimate | 118M params × 2 bytes |
| GGUF Q8_0 | 0.13 GB | File size | cstr/multilingual-e5-small-GGUF |
File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.
GPU requirements: deploy multilingual-e5-small on your hardware
| Hardware | BF16 (full precision) | GGUF Q8_0 |
|---|---|---|
| NVIDIA H200141 GB | 1× | 1× |
| NVIDIA H10080 GB | 1× | 1× |
| NVIDIA A100 80GB80 GB | 1× | 1× |
| NVIDIA L40S48 GB | 1× | 1× |
GPUs needed per variant.
| Hardware | GGUF Q8_0 |
|---|---|
| RTX 509032 GB | 1× |
| RTX 409024 GB | 1× |
| RTX 309024 GB | 1× |
GGUF via llama.cpp; more cards or CPU offload for larger files.
| Hardware | GGUF Q8_0 |
|---|---|
| Mac, 512 GBM3 Ultra | Fits |
| Mac, 192 GBM2 Ultra | Fits |
| Mac, 128 GBM4 Max / M3 Max | Fits |
| Mac, 64 GBM4 Pro / Max | Fits |
Assumes ~75% of unified memory is usable by the GPU.
| Hardware | BF16 (full precision) | GGUF Q8_0 |
|---|---|---|
| AWS p5.48xlarge8× H100 · 640 GB | Fits | Fits |
| AWS p4de.24xlarge8× A100 80GB · 640 GB | Fits | Fits |
| AWS g6e.12xlarge4× L40S · 192 GB | Fits | Fits |
| Google Cloud a3-highgpu-8g8× H100 · 640 GB | Fits | Fits |
| Azure ND H100 v58× H100 · 640 GB | Fits | Fits |
Whole-instance GPU memory; public instance specs.
Not on LLM.API yet
We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.
Not available on LLM API yet
We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.
Browse available modelsWhen to Use — When NOT to Use
Use it if...
- You need vectors for semantic search, clustering or RAG retrieval (multilingual-e5-small)
- You want to keep documents inside your own network while indexing them
- The weights are published openly, so you can pin one checkpoint and keep vectors reproducible
- You are comparing self-hosting cost against a managed embedding endpoint
Avoid if...
- LLM API does not route this model today, so there is no endpoint here to call
- You want zero operations — serving, scaling and upgrades are yours to run
- You need text generation — this model returns vectors or scores, not answers
- You already have an index built with a different model: vectors are not interchangeable
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