NVIDIA, United States · Released October 16, 2026Nemotron Labs Dory 1.0 4B at a glance
- Made by
- NVIDIA, United States
- Released
- October 16, 2026
- Parameters
- 3.98B
- Context window
- 256K tokens
Nemotron Labs Dory 1.0 4B VRAM Requirements
NVIDIA, United States · Released October 16, 2026New
Nemotron Labs Dory 1.0 4B has 3.98B parameters. In BF16 its weights alone take 7.41 GiB; quantized to 4-bit, about 2.31 GiB. Each 32K-token request adds 1.91 GiB of KV cache. Cheapest way to run it today: 1 × RTX A4000 (16 GB) at $0.088 per hour.
Size your own setup in the calculator →VRAM by precision and context length
Weights plus the KV cache of one request (BF16 cache), in GiB. Add 1 to 3 GiB for the inference engine itself.
| Weights format | Weights only | + 8K context | + 32K context | + 128K context | + 256K context |
|---|---|---|---|---|---|
| BF16 / FP16 | 7.41 | 8.01 | 9.33 | 14.6 | 21.6 |
| FP8 / INT8 | 4.33 | 4.93 | 6.25 | 11.5 | 18.5 |
| 4-bit (GGUF Q4_K_M) | 2.31 | 2.91 | 4.22 | 9.47 | 16.5 |
How many GPUs to run Nemotron Labs Dory 1.0 4B
Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.
| GPU | BF16 / FP16 | 8-bit | 4-bit |
|---|---|---|---|
| GeForce RTX 4090 24 GB | 1 | 1 | 1 |
| GeForce RTX 5090 32 GB | 1 | 1 | 1 |
| RTX PRO 6000 Blackwell 96 GB | 1 | 1 | 1 |
| Mac M5 Max 128 GB 96 GB usable | 1 | 1 | 1 |
| Ryzen AI Max+ 395 128 GB 96 GB usable | 1 | 1 | 1 |
| DGX Spark (GB10) 128 GB | 1 | 1 | 1 |
| NVIDIA H100 80 GB | 1 | 1 | 1 |
| NVIDIA H200 141 GB | 1 | 1 | 1 |
| NVIDIA B200 (HGX) 180 GB | 1 | 1 | 1 |
8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size.
Cheapest way to run Nemotron Labs Dory 1.0 4B today
For each GPU: the fewest cards that fit Nemotron Labs Dory 1.0 4B with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-19).
| Setup | Per hour | Weights | Provider | Per month | Rent |
|---|---|---|---|---|---|
| 1 × RTX A4000 16 GB | $0.088 | BF16 | Vast.ai | $64.24 | Rent 1 × RTX A4000 on Vast.ai (opens in a new tab) → |
| 1 × GeForce RTX 3090 24 GB | $0.134 | BF16 | Vast.ai | $97.82 | Rent 1 × GeForce RTX 3090 on Vast.ai (opens in a new tab) → |
| 1 × GeForce RTX 4080 / 4080 SUPER 16 GB | $0.150 | BF16 | Akash Network | $109.50 | Rent 1 × GeForce RTX 4080 / 4080 SUPER on Akash Network (opens in a new tab) → |
| 1 × GeForce RTX 5080 16 GB | $0.201 | BF16 | Vast.ai | $146.73 | Rent 1 × GeForce RTX 5080 on Vast.ai (opens in a new tab) → |
| 1 × RTX A5000 24 GB | $0.214 | BF16 | Vast.ai | $156.22 | Rent 1 × RTX A5000 on Vast.ai (opens in a new tab) → |
| 1 × RTX 2000 Ada 16 GB | $0.240 | BF16 | RunPod | $175.20 | Rent 1 × RTX 2000 Ada on RunPod (opens in a new tab) → |
| 1 × RTX PRO 4000 Blackwell 24 GB | $0.243 | BF16 | Vast.ai | $177.39 | Rent 1 × RTX PRO 4000 Blackwell on Vast.ai (opens in a new tab) → |
| 1 × RTX A4500 20 GB | $0.250 | BF16 | RunPod | $182.50 | Rent 1 × RTX A4500 on RunPod (opens in a new tab) → |
On-demand prices from AWS, Google Cloud, Azure, RunPod, Vast.ai, Oracle Cloud, OVHcloud, Scaleway, Verda, Akash and UpCloud, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.
AWS, Google Cloud and UpCloud prices: Spare Cores (opens in a new tab), CC BY-SA 4.0 (opens in a new tab), shared under the same license.
KV cache: how memory grows with context
Architecture: Looped transformer (16 input + 40 recurrent × 2 loops + 16 output sub-layers) · 14 full-attention + 42 sliding-window caches (1024 tokens), GQA 4 KV × 256. Its KV cache is 4.7× smaller than if every layer used full attention, because most layers only keep a short window of recent tokens.
| Context per request | BF16 cache | FP8 cache |
|---|---|---|
| 8K tokens | 0.60 GiB | 0.30 GiB |
| 32K tokens | 1.91 GiB | 0.96 GiB |
| 128K tokens | 7.16 GiB | 3.58 GiB |
| 256K tokens | 14.2 GiB | 7.08 GiB |
Nemotron Labs Dory 1.0 4B VRAM FAQ
How much VRAM does Nemotron Labs Dory 1.0 4B need?
In BF16 the weights alone take 7.41 GiB (8.0 GB). In FP8 that is 4.33 GiB, and about 2.31 GiB with 4-bit quantization (Q4_K_M). Each request then adds KV cache: 0.60 GiB at 8K tokens and 1.91 GiB at 32K (BF16 cache).
Can Nemotron Labs Dory 1.0 4B run on a single RTX 4090 (24 GB)?
Yes. In BF16 it fits on one RTX 4090 with an 8K-token context (8.01 GiB including the cache, with llama.cpp).
Can Nemotron Labs Dory 1.0 4B run on an RTX 5090 (32 GB)?
Yes. In BF16 (8.01 GiB with an 8K-token context) it fits on one RTX 5090, with llama.cpp.
Can Nemotron Labs Dory 1.0 4B run on a Mac?
Yes. In 4-bit (Q4_K_M with llama.cpp) it needs a Mac with 8 GB of unified memory or more, of which macOS gives the GPU about 5 GB, for one request with an 8K-token context. In 8-bit (Q8_0) it needs 16 GB or more.
Can Nemotron Labs Dory 1.0 4B run on a DGX Spark or a Ryzen AI Max+ 395 (128 GB)?
Yes. In BF16 (8.01 GiB with an 8K-token context) it fits on a DGX Spark (128 GB) and on a Ryzen AI Max+ 395 with 128 GB, whose GPU gets up to 96 GB, with llama.cpp and one request.
What is the cheapest way to run Nemotron Labs Dory 1.0 4B?
On 2026-10-19, the cheapest on-demand setup is 1 × RTX A4000 (16 GB) with BF16 weights on Vast.ai, at $0.088 per hour (about $64.24 per month). Next: 1 × GeForce RTX 3090 (24 GB) with BF16 weights on Vast.ai, at $0.134 per hour. Sized for one 8K-token request; prices are checked every hour.
How many H100 GPUs does Nemotron Labs Dory 1.0 4B need?
With one request and an 8K-token context: 1 in BF16, 1 in FP8 and 1 in 4-bit (vLLM, 90% of memory usable). Serving many users at once needs more memory for their KV caches: the calculator sizes that for you.
Nemotron Labs Dory 1.0 4B VRAM badge
For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/nemotron-labs-dory-1-0-4b.json.
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Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.