NVIDIA · Chat and reasoning · 3.98B parameters · OpenMDW-1.1
NVIDIA Nemotron-Labs Dory1.0-4B
Needs 5 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.
Parameters3.98Bdense
Context256k tokensmaximum published
Checkpoint8 GBBF16, as published
LicenseOpenMDW-1.1check before commercial use
ReleasedOct 16, 2026documented date
Smallest configuration · FP8 / INT8
1× Quadro P20005 GB VRAM · 96% used
- Per hour
- $0.03
- Per month
- $22
- Cheapest at
Vast.ai
What is documented about NVIDIA Nemotron-Labs Dory1.0-4B
news checked 2026-10-27Research 'looped (latent-recurrent)' LLM: 8 input layers + 20 recurrent layers applied several times + 8 output layers, with elastic inference compute. config lists 72 sub-layers (36 attention + 36 MLP). Compute per token, but not weight memory, grows with the number of loops. Card 'Release Date: October 16, 2026'; up to 256K context, English.
- https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-Dory1.0-4B-BF16
- https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-Dory1.0-4B-BF16/raw/main/config.json
- https://huggingface.co/api/models/nvidia/NVIDIA-Nemotron-Labs-Dory1.0-4B-BF16?blobs=true
Memory · 3 precisions · 5 context lengthsHow 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 | 8 GB | 10 GB | 11 GB | 16 GB | 34 GB | 59 GB |
| FP8 / INT8estimated | 4 GB | 5 GB | 6 GB | 11 GB | 30 GB | 54 GB |
| INT4 / MXFP4estimated | 2 GB | 3 GB | 4 GB | 10 GB | 28 GB | 52 GB |
| KV cacheone sequence, FP16 cache, included above | — | — | 1 GB | 7 GB | 25 GB | 49 GB |
Cross-checked on Hugging Face Oct 28, 2026: 3.98B parameters in the safetensors headers, 8.0 GB of weight files, attention layout from config.json (72 layers, 4 KV heads × 256, 27 sliding-window layers). 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 × 45 full-attention layers + 27 sliding-window layers capped at 1024 tokens (FP16 cache, one sequence).config.json ↗
Smallest configuration per GPU · 110 GPUs · cheapest eligible on-demand offer per GPU count, every provider on fileWhat 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 | 96% tight | $0.03 | $22 | Vast.aiTrust 78 · read Nov 1 · receipt |
| RTX 3060 | 1× 12 GB12 GB total | 40% | $0.04 | $26 | Vast.aiTrust 78 · read Nov 1 · receipt · also Clore.ai $0.05, gcube $0.05 |
| GTX 1660 | 1× 6 GB6 GB total | 80% | $0.04 | $26 | Vast.aiTrust 78 · read Nov 1 · receipt · also Edgevana $0.78 |
| RTX A2000 | 1× 12 GB12 GB total | 40% | $0.04 | $26 | Vast.aiTrust 78 · read Nov 1 · receipt · also gcube $0.09 |
| GTX 1660 Ti | 1× 6 GB6 GB total | 80% | $0.04 | $26 | Vast.aiTrust 78 · read Nov 1 · receipt · also Theta EdgeCloud $0.12 |
| Tesla P4 | 1× 8 GB8 GB total | 60% | $0.04 | $29 | Vast.aiTrust 78 · read Nov 1 · receipt · also Akash Network $0.06, gcube $0.07 |
| T4 | 1× 16 GB16 GB total | 30% | $0.04 | $29 | Vast.aiTrust 78 · read Nov 1 · receipt · also iwinv $0.20, Theta EdgeCloud $0.20 |
| GTX 1060 | 1× 6 GB6 GB total | 80% | $0.04 | $30 | Vast.aiTrust 78 · read Nov 1 · receipt |
| Tesla P40 | 1× 24 GB24 GB total | 20% | $0.04 | $30 |  Clore.aiTrust 46 · read Nov 1 · receipt · also gcube $0.07, Vast.ai $0.11 |
| GTX Titan X | 1× 12 GB12 GB total | 40% | $0.04 | $31 | Vast.aiTrust 78 · read Nov 1 · receipt |
| Titan Xp | 1× 12 GB12 GB total | 40% | $0.04 | $31 | Vast.aiTrust 78 · read Nov 1 · receipt |
| GTX 1080 | 1× 8 GB8 GB total | 60% | $0.04 | $32 | Vast.aiTrust 78 · read Nov 1 · receipt · also Edgevana $0.06 |
| Tesla P100 | 1× 16 GB16 GB total | 30% | $0.04 | $33 | Vast.aiTrust 78 · read Nov 1 · receipt · also gcube $0.07 |
| RTX 2060 | 1× 6 GB6 GB total | 80% | $0.04 | $33 | |
| GTX 1070 | 1× 8 GB8 GB total | 60% | $0.05 | $36 | Vast.aiTrust 78 · read Nov 1 · receipt · also Clore.ai $0.08 |
| GTX 1660 Super | 1× 6 GB6 GB total | 80% | $0.05 | $38 |  Theta EdgeCloudnot scored · read Nov 1 · receipt |
| RTX 4060 | 1× 8 GB8 GB total | 60% | $0.05 | $40 | Vast.aiTrust 78 · read Nov 1 · receipt · also Clore.ai $0.08, Theta EdgeCloud $0.09 |
| RTX 2070 | 1× 8 GB8 GB total | 60% | $0.06 | $41 | Vast.aiTrust 78 · read Nov 1 · receipt · also gcube $0.06, Clore.ai $0.21 |
| RTX 3060 Ti | 1× 8 GB8 GB total | 60% | $0.06 | $41 | Vast.aiTrust 78 · read Nov 1 · receipt · also gcube $0.06, Clore.ai $0.06 |
| RTX 2060 Super | 1× 8 GB8 GB total | 60% | $0.06 | $41 | |
| GTX 1070 Ti | 1× 8 GB8 GB total | 60% | $0.06 | $44 | Vast.aiTrust 78 · read Nov 1 · receipt · also Clore.ai $0.10 |
| RTX 2070 Super | 1× 8 GB8 GB total | 60% | $0.06 | $44 | |
| RTX 3070 | 1× 8 GB8 GB total | 60% | $0.06 | $44 | |
| GTX 1080 Ti | 1× 11 GB11 GB total | 43% | $0.06 | $45 | Vast.aiTrust 78 · read Nov 1 · receipt · also Edgevana $0.08 |
| The 24 cheapest of 107 GPU types that fit. Every GPU, any precision and context, in the finder | |||||
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