NVIDIA · Embeddings and rerank · 1.55B parameters · OpenMDW-1.1
Nemotron-3.5-Embed-1B
Needs 2 GB at FP8 / INT8. The smallest real configuration today is 1× Quadro P2000 at Vast.ai, $0.03 per hour, about $22 a month at 730 hours.
Parameters1.55Bdense
Context256k tokensmaximum published
Checkpoint3 GBBF16, as published
LicenseOpenMDW-1.1check before commercial use
ReleasedOct 20, 2026documented date
Smallest configuration · FP8 / INT8
1× Quadro P20005 GB VRAM · 37% used
- Per hour
- $0.03
- Per month
- $22
- Cheapest at
Vast.ai
What is documented about Nemotron-3.5-Embed-1B
news checked 2026-10-27Small multimodal sibling of the 8B. HF repo created 2026-10-07; release per Nemotron 3.5 retrieval launch 2026-10-20/21.
- https://huggingface.co/nvidia/Nemotron-3.5-Embed-1B-BF16
- https://huggingface.co/api/models/nvidia/Nemotron-3.5-Embed-1B-BF16?blobs=true
- https://www.unite.ai/nvidia-debuts-open-nemotron-3-5-models-for-embedding-reranking-parsing/
How much VRAM it needs
Weights plus runtime overhead, then the KV cache that grows with the context you serve.
| Precision | Weights | Weights + headroom | 4k context | 32k context | 128k context | 256k context |
|---|---|---|---|---|---|---|
| BF16published checkpoint | 3 GB | 4 GB | 4 GB | 6 GB | 13 GB | 21 GB |
| FP8 / INT8estimated | 2 GB | 2 GB | 3 GB | 5 GB | 11 GB | 20 GB |
| INT4 / MXFP4estimated | 1 GB | 2 GB | 2 GB | 4 GB | 10 GB | 19 GB |
| KV cacheone sequence, FP16 cache, included above | — | — | 1 GB | 3 GB | 9 GB | 18 GB |
Cross-checked on Hugging Face Oct 28, 2026: 1.55B parameters in the safetensors headers, 3.1 GB of weight files, attention layout from config.json (16 layers, 8 KV heads × 128). Method: weights as published at the native precision, verified on Hugging Face 2026-10-28; other precisions estimated at 1 (8-bit) and 0.6 (4-bit) bytes per parameter; × 1.2 runtime headroom; KV cache = 4096 bytes per token per layer × 16 full-attention layers (FP16 cache, one sequence).config.json ↗
What it costs to run, GPU by GPU
The fewest GPUs of one type whose total memory fits the model at short context, priced from the cheapest eligible on-demand offer for exactly that count (45-day window, newest reading per provider), with the next providers beside it. Monthly = 730 hours.
| GPU | Smallest config | VRAM used | $ / hour | $ / month | Cheapest at |
|---|---|---|---|---|---|
| Quadro P2000Best fit | 1× 5 GB5 GB total | 37% | $0.03 | $22 | |
| RTX 3060 | 1× 12 GB12 GB total | 16% | $0.04 | $26 | |
| RTX A2000 | 1× 12 GB12 GB total | 16% | $0.04 | $26 | |
| GTX 1660 Ti | 1× 6 GB6 GB total | 31% | $0.04 | $26 | |
| GTX 1050 | 1× 2 GB2 GB total | 93% tight | $0.04 | $27 | |
| Tesla P4 | 1× 8 GB8 GB total | 23% | $0.04 | $29 | |
| T4 | 1× 16 GB16 GB total | 12% | $0.04 | $29 | |
| GTX 1060 | 1× 6 GB6 GB total | 31% | $0.04 | $30 | |
| GTX 1660 | 1× 6 GB6 GB total | 31% | $0.04 | $30 | |
| Tesla P40 | 1× 24 GB24 GB total | 8% | $0.04 | $30 | |
| GTX Titan X | 1× 12 GB12 GB total | 16% | $0.04 | $31 | |
| Titan Xp | 1× 12 GB12 GB total | 16% | $0.04 | $31 | |
| GTX 1080 | 1× 8 GB8 GB total | 23% | $0.04 | $32 | |
| Tesla P100 | 1× 16 GB16 GB total | 12% | $0.04 | $33 | |
| RTX 2060 | 1× 6 GB6 GB total | 31% | $0.04 | $33 | |
| GTX 1650 | 1× 4 GB4 GB total | 47% | $0.05 | $35 | |
| GTX 1070 | 1× 8 GB8 GB total | 23% | $0.05 | $36 | |
| GTX 1660 Super | 1× 6 GB6 GB total | 31% | $0.05 | $38 | |
| Quadro P4000 | 1× 8 GB8 GB total | 23% | $0.06 | $40 | |
| RTX 2070 | 1× 8 GB8 GB total | 23% | $0.06 | $41 | |
| RTX 3060 Ti | 1× 8 GB8 GB total | 23% | $0.06 | $41 | |
| RTX 2060 Super | 1× 8 GB8 GB total | 23% | $0.06 | $41 | |
| GTX 1070 Ti | 1× 8 GB8 GB total | 23% | $0.06 | $44 | |
| RTX 2070 Super | 1× 8 GB8 GB total | 23% | $0.06 | $44 | |
| The 24 cheapest of 110 GPU types that fit. Every GPU, any precision and context, in the finder | |||||
