What is the difference between DeepSeek-R1 and Nemotron 3.5 Lightning 30B A3B?

DeepSeek-R1 (DeepSeek) features 671B MoE (37B active) with a 128k context window, while Nemotron 3.5 Lightning 30B A3B (NVIDIA) features 30B with a 1000k context window.

Which is cheaper for production inference, DeepSeek-R1 or Nemotron 3.5 Lightning 30B A3B?

DeepSeek-R1 input pricing is $0.55 per 1M tokens vs Nemotron 3.5 Lightning 30B A3B at Open / Free per 1M tokens.

Can I run DeepSeek-R1 or Nemotron 3.5 Lightning 30B A3B locally on private GPU hardware?

DeepSeek-R1 is a closed cloud API and cannot be self-hosted. Nemotron 3.5 Lightning 30B A3B is an open-weights model and can be hosted locally.

Back to LLM Benchmark & Hardware Sizing Engine

DeepSeekvsNVIDIA

DeepSeek-R1 vs Nemotron 3.5 Lightning 30B A3B

Side-by-side technical showdown between DeepSeek-R1 and Nemotron 3.5 Lightning 30B A3B on TheModelverse. Compare verified LLM benchmark scores, quantization compression, local GPU hardware sizing, and API inference pricing.

Executive Showdown & Winner Breakdown

Who Wins Where: DeepSeek-R1 vs Nemotron 3.5 Lightning 30B A3B

Verified across benchmarks, pricing & local VRAM footprint

Reasoning & STEM Intelligence

+5.8% lead

Leader:DeepSeek-R1(79.8 vs 75.4)

DeepSeek-R1 outperforms in advanced scientific and math problem-solving benchmarks.

Software Engineering & Code

+79.4% lead

Leader:DeepSeek-R1(92.5 vs 51.6)

DeepSeek-R1 demonstrates higher code generation accuracy and agentic bug resolution.

Local Portability & Sovereignty

Self-Hostable

Leader:Nemotron 3.5 Lightning 30B A3B(Open Weights vs Closed API)

Nemotron 3.5 Lightning 30B A3B can be deployed on private GPUs, Ollama, and on-prem clusters without third-party vendor lock-in.

Hardware Sizing & Model Compression

LLM Hardware Sizing & Quantization Sizing

Simulate weight compression levels and dynamic KV-cache expansion to verify whether these models fit on your local hardware or cloud GPU cluster.

Quantization Level

Active Precision

4 bits / parameter

VRAM Reduction

-72.5% vs FP16

Benchmark Retention

96–98% (Sweet Spot)

Context Simulator

Total VRAM Footprint @ 8k Context (INT4 (GGUF / AWQ))

Model 1Cloud Hosted

DeepSeek-R1

Zero Local VRAM

Inference runs fully on provider cloud infrastructure. Zero GPU required locally.

Total Params:

671B MoE (37B active)

Active Compute:

Dense

Attention Scheme:

MLA (Multi-Head Latent)

Max Context:

128k tokens

Target Hardware: Zero Local VRAM (Managed Cloud API)

Model 2Open Weights

Nemotron 3.5 Lightning 30B A3B

19GB

Weights: ~16.5 GBKV Cache: ~0 GB+15% CUDA Buffer

Total Params:

30B

Active Compute:

3B (Sparse MoE)

Attention Scheme:

GQA (Grouped-Query)

Max Context:

1000k tokens

Target Hardware: 1x RTX 3090 / 4090 (24GB) or Mac (32GB Unified)

GPU & Hardware Tier Compatibility Matrix

Hardware Configuration	Available VRAM	DeepSeek-R1	Nemotron 3.5 Lightning 30B A3B
16 GB VRAM RTX 4070 / 4080 (16GB), Mac M-Series (16GB)	16 GB	Cloud API	✕ OOM
24 GB VRAM 1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB)	24 GB	Cloud API	Optimal
48 GB VRAM 2x RTX 4090 (TP=2), 1x L40S, Mac M-Series (64GB)	48 GB	Cloud API	Optimal
80 GB VRAM 1x NVIDIA A100 / H100 (80GB), Mac Studio (128GB)	80 GB	Cloud API	Optimal
160 GB Node 2x H100 (TP=2), 4x L40S, Mac Studio (192GB)	160 GB	Cloud API	Optimal
Multi-Node Cluster 4x–8x H100 Datacenter Cluster	320 GB	Cloud API	Optimal

LLM Benchmark Database

LLM Benchmark Showdown & Head-to-Head Deltas

Normalized evaluation scores across code generation, advanced reasoning, mathematics, and multidisciplinary exams.

SWE-bench (Coding)Score % / Points

DeepSeek-R1

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