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Nemotron
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  3. /Nemotron-Labs Diffusion 8B
N
ChatCodingReasoningMulti-purpose8.5BDiffusion LM
Chat

Nemotron-Labs Diffusion 8B

Nemotron · NVIDIA Nemotron Open Model License

Diffusion language model with Autoregressive (AR), Diffusion, Self-speculation. Default matrix mode (when available): Self-speculation.

Diffusion language model with autoregressive, diffusion, and self-speculation decode modes. Official weights ship as BF16 Safetensors (~17 GB) with custom architecture code; no official GGUF. Consumer path today is Python transformers>=5.0 with trust_remote_code or pending SGLang DLM support. Not in our compatibility matrix until a reproducible llama.cpp or Ollama path exists. VRAM figures are estimated from official weight size, not benchmarked on OwnRig hardware.

Parameters
8.5B
Architecture
Diffusion LM
Context
32,768 tokens
Released
2026-05-23
Engines
transformers (custom), sglang (preview)

Parameters

8.5B

VRAM

19 GB

Context

32K

Formats

1

GPUs

Pending — GPU compatibility ratings not published yet

Nemotron-Labs Diffusion 8B (8.5B) requires 19 GB VRAM at recommended quality (FP16). For the best experience, AMD AI Powerhouse ($1,818) is recommended.

Source: OwnRig methodology

Compatibility not published yet

Nemotron-Labs Diffusion 8B is in our catalog for reference. We add GPU fit and speed ratings only after a reproducible consumer runtime exists (stock llama.cpp, Ollama, or stable SGLang DLM). Today most builders still need transformers with custom CUDA code.

How diffusion language models differ from standard chat models

VRAM (Recommended)

19 GB

Quantization

FP16

File Size

16.98 GB

Max Context

32K tokens

Primary Use

Chat

Scaling

Context Length Impact

KV cache VRAM at FP16 quality. Longer context = more memory.

ContextKV CacheTotal VRAM
2K205 MB19.2 GB
4K410 MB19.4 GB
8K717 MB19.7 GB
16K1.4 GB20.4 GB
32K2.9 GB21.9 GB
FAQ

Frequently Asked Questions

How much VRAM does Nemotron-Labs Diffusion 8B need?
Nemotron-Labs Diffusion 8B requires 19 GB VRAM at recommended quality (FP16).
Is Nemotron-Labs Diffusion 8B good for coding?
Nemotron-Labs Diffusion 8B supports coding use cases. For the best coding experience, pair it with an embedding model for local RAG.

Related Guides

Explainer

What are diffusion language models?

Diffusion LMs rewrite text blocks in parallel, not one token at a time. What that means for VRAM, speed claims, and running Nemotron on a gaming GPU today.

All models

Data confidence: estimated. Source

VRAM requirements are calculated from model parameters and may vary by inference engine, context length, and batch size. Performance estimates are based on community benchmarks and should be verified for your specific configuration.Nemotron is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.