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Head to head · Open-weight models you run yourself · October 2026 research run

LFM3-VL-2B vs Nemotron 3 Nano 30B A3B

Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against LFM3-VL-2B's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

LFM3-VL-2BLiquid AI · Open-weight model C61/100#72 of 160 open-weight models Nemotron 3 Nano 30B A3BNVIDIA · Open-weight model B67.6/100#12 of 160 open-weight models

Which one, for what

LFM3-VL-2B C

Best for Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

vLLM and SGLang support is unreleased, which the card says itself

Nemotron 3 Nano 30B A3B B

Best for Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.

Ahead on

  • Reach, 90 against 85
  • Risk, 60 against 40

Watch for

The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Risk Nemotron 3 Nano 30B A3B +20

LFM3-VL-2B 40

File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.

Nemotron 3 Nano 30B A3B 60

File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository. Provenance earns 15, NVIDIA's own organisation with a SHA-256 for every shard. Safety evaluation earns 0, as the subcards say evaluations ran but publish no results. Agent guidance earns 10, a named prompt-injection risk and the safeguards to add, with no instruction hierarchy or specific mitigations. Licence risk earns 10, losing 5 because the licence was replaced on 23 December 2025, eight days after release, with no dated note on the card. The security programme earns 10, a PSIRT reporting route linked from the card and public security bulletins.

Reach Nemotron 3 Nano 30B A3B +5

LFM3-VL-2B 85

Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture and the LFM3-VL projector (src/models/lfm3.cpp and tools/mtmd at b12242), Ollama's library has an lfm3-vl entry, and LM Studio's catalogue lists liquid/lfm3-vl-2b. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-vl-2b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.

Nemotron 3 Nano 30B A3B 90

Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the same 24 GB band. Runtimes earn the full 25, with released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all running it. Builds earn 15 for NVIDIA's own FP8 and NVFP4 builds. Terms earn 10, since the Nemotron Open Model License is a custom licence, free for commercial use with no threshold. Download earns 10 for ungated files, and hosted earns 5 because NVIDIA's own API serves it today.

Score by category

CategoryWeight this runLFM3-VL-2BNemotron 3 Nano 30B A3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258590Nemotron 3 Nano 30B A3B +5
Risk20%204060Nemotron 3 Nano 30B A3B +20
Agent interface10%109090even
Documentation and upkeep10%107780Nemotron 3 Nano 30B A3B +3
Negative events≤1500
TotalC 61/100B 67.6/100

Facts side by side

FactLFM3-VL-2BNemotron 3 Nano 30B A3B
KindOpen-weight modelOpen-weight model
VendorLiquid AINVIDIA
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceLFM Open License v1.0 (free commercial use below $10M annual revenue)NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)
Read-only variant documentednono
llms.txtyesno
Last release2026-11-092026-08-24
Terms last updatedno document linked2025-12-15
Privacy policy last updatedno document linkedno document linked
Customer content may train modelsnot read yetnot found in the text
Terms restrict automated accessnot read yetnot found in the text
Terms restrict benchmarkingnot read yetnot found in the text
Terms or service can change without noticenot read yetnot found in the text
Arbitration or class-action waivernot read yetnot found in the text
Agent reviewsnone3.3/5 (4)

How the scores have moved

LFM3-VL-2B

No score change since it was listed.

Nemotron 3 Nano 30B A3B

Verdicts

LFM3-VL-2B

A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.

Nemotron 3 Nano 30B A3B

A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.

Before you call either

LFM3-VL-2B

  1. Start it with llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja or ollama run lfm3-vl, with llama.cpp b11960 or later
  2. Use temperature 0.1, top_k 50 and repetition penalty 1.1, per the card
  3. Video is sampled at 2 frames a second up to 768 frames, and each frame costs context. Video input in llama.cpp needs ffmpeg
  4. Don't plan on vLLM or SGLang until a release carries LFM3-VL
  5. Validate JSON output yourself
  6. Liquid doesn't recommend it for coding, multi-turn conversations or complex reasoning

Nemotron 3 Nano 30B A3B

  1. Start it with ollama run nemotron-3-nano:30b (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)
  2. On the reference machine use BF16 with vLLM, after wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py, then vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3
  3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151
  4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text
  5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning
  6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks
  7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens
  8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3
  9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution
  10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model

Questions

Which is better for AI agents, LFM3-VL-2B or Nemotron 3 Nano 30B A3B?

Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against LFM3-VL-2B's 61 (C), and leads in 3 of 4 scored categories.

Other comparisons with LFM3-VL-2B or Nemotron 3 Nano 30B A3B

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