LOADING DATASHEET
LOADING DATASHEET
by Nvidia
RANKED #229 OF 316 ASSISTANTS · OVERALL #346 OF 493 RANKED · VIBE SCORE 5.6 · 464 VOICES ·
COPY BADGE pastes a README snippet with the rank SVG.
Flagship model for demanding analysis, coding, and production agent workflows
36 mentions
13 mentions
26 weeks · 449 voices in this window
HONEYMOON FADING
Early praise is cooling off in recent weeks.
Open weights. You can use a hosted service, or download it and run it yourself, free.
Open weights: download BGE M3 once and it's yours. No subscription, no rate limits, works offline.
560M parameters, FP16 file is about 1.2 GB, Q4 file is about 440 MB
Intel Core i5-10400 Desktop PC with integrated graphics
8 GB RAM
~$180 USEDCPU inference with Q4 quant, ~450 tok/s processing speed, 8k context
Nvidia GeForce RTX 3060
12 GB VRAM
~$260 USEDFull FP16 precision, in-VRAM embedding extraction, ~3200 tok/s, 8k context
Nvidia GeForce RTX 4070 SUPER
12 GB VRAM
~$590 NEWFull FP16 precision, maximum batch throughput, ~9500 tok/s, 8k context
The three cards above are researched picks. Search the machine you actually have — we only say yes if it should reply at a usable speed, not just load the weights and crawl.
HARDWARE PICKS & PRICES ARE RESEARCHED FROM THE LIVE WEB AND REFRESHED AUTOMATICALLY. TREAT THEM AS BALLPARK, NOT GOSPEL.
Everyday jobs on each of those rigs: how long BGE M3 takes, and how good the crowd says it is at that kind of work.
| THE JOB | BARE MINIMUMIntel Core i5-10400 Desktop PC with integrated graphics | SWEET SPOTNvidia GeForce RTX 3060 | FULL POWERNvidia GeForce RTX 4070 SUPER |
|---|---|---|---|
| Write an emaila paragraph or two, drafted from a one-line brief | ~1 S★★★★★ | UNDER A SECOND★★★★★ | UNDER A SECOND★★★★★ |
| Summarize a documenta long report boiled down to the points that matter | ~3 S★★★★★ | UNDER A SECOND★★★★★ | UNDER A SECOND★★★★★ |
| Build a websitea small landing page, markup and styles together | ~18 S★★★★★ | ~3 S★★★★★ | UNDER A SECOND★★★★★ |
| Build a backendan API with routes, storage and tests | ~56 S★★★★★ | ~8 S★★★★★ | ~3 S★★★★★ |
| Build a gamea playable browser game in one file | ~33 S★★★★★ | ~5 S★★★★★ | ~2 S★★★★★ |
STARS ARE THE COMMUNITY SCORE FOR THAT KIND OF WORK, DOCKED FOR HOW SQUASHED THE WEIGHTS GET AT EACH TIER. TIMES ARE COMPUTED FROM RESEARCHED SPEEDS. BALLPARK, NOT GOSPEL.
People are mostly frustrated with BGE M3 right now.
416 POSTS · 48 COMMENTS · GITHUB · DEV.TO · DEV FORUMS · REDDIT · LEMMY · BLUESKY
0 thumbs up · 4 thumbs down
Other models the crowd has fully reviewed, starting with text models like this one.
I built an open-source RAG system that actually understands images, tables, and document structure — not just text chunks
Fine-tuning Embedding models in Unsloth!
Best embedding model for indexing ~17,000 scientific PDFs for a RAG system in 2026?
RAG at scale still underperforming for large policy/legal docs – what actually works in production?
Stop Fine-Tuning Embedding Models Right Away. Run This Checklist First. Saved Me Weeks
Hybrid search (BM25 + vectors + RRF) barely improved over pure semantic on 600 technical docs. What am I missing?
With all the other news today (Mistral 4, local Qwen 3.8 Next Flash) this kind of got missed. In process of migrating a
Legal RAG issues
Looking for testers: 100% local RAG system with one-command setup
Graph RAG: anyone actually scaled it past a few thousand docs in production?
Open WebUI RAG at scale still underperforming for large policy/legal docs – what actually works in production?
Fully offline multi-modal RAG for NASA Life Sciences PDFs + images + audio + knowledge graphs – best 2025 local stack?