Head to head · Open-weight models you run yourself · October 2026 research run
LFM3-Audio-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
LFM3-Audio-2B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons
Which one, for what
Best for Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang
NVIDIA Nemotron 3.5 Bolt E4B 4GB C
Best for Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.
Ahead on
- Reach, 90 against 80
- Risk, 80 against 40
- Documentation and upkeep, 65 against 52
Watch for
64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway
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 NVIDIA Nemotron 3.5 Bolt E4B 4GB +40
LFM3-Audio-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.
NVIDIA Nemotron 3.5 Bolt E4B 4GB 80
File safety earns 25, safetensors and GGUF only, and the documented routes run no repository code (an undocumented Inference Endpoints handler.py sits in the BF16 repository). Provenance earns 15, NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA names its content-safety and bias datasets but publishes no results. Agent guidance earns 15, for instruction-hierarchy training NVIDIA documents and a subcard on prompt injection and the safeguards to add. Licence risk keeps all 15 under OpenMDW-1.1 and Apache 2.0. The security programme earns 10 for the card's reporting route and NVIDIA's public bulletins.
Documentation and upkeep NVIDIA Nemotron 3.5 Bolt E4B 4GB +13
LFM3-Audio-2B 52
Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), limitations (not recommended for agentic coding or complex multi-step reasoning, voice chat in English only), an architecture section, parameters (2B), context (32K) and 19 speech languages and locales. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 31 October, edited the card. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (27 and 31 October 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 17 + 5 + 20 + 0 + 5 + 5 = 52.
NVIDIA Nemotron 3.5 Bolt E4B 4GB 65
The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 15, the RL datasets published and a cut-off given, but the card's data section describes a larger SFT corpus the report says wasn't used, and the base's pre-training is described only in general. Last change earns 20 for 23 October 2026. Responsiveness earns 0, as NVIDIA hasn't answered either of the two discussions. Version policy earns 5 as a first release whose GGUF was replaced on release day with only a commit title. File health earns 5, since the card and the template disagree on the thinking default and both cards carry leftover lines.
Reach NVIDIA Nemotron 3.5 Bolt E4B 4GB +10
LFM3-Audio-2B 80
Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.17 GB projector file, and 1.27 GB × 1.2 = 1.5 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 audio projector and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example that starts this model), and Ollama's library has an lfm3-audio entry. 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-audio-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.
NVIDIA Nemotron 3.5 Bolt E4B 4GB 90
Memory earns 30, since NVIDIA's Q4_K_M GGUF is 6.18 GB, 7.4 GB with a fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, Transformers, SGLang, Ollama and LM Studio. Builds earn 10, as NVIDIA's only quantised build is the Q4_K_M GGUF and the DFlash repositories are drafters. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence, and the Gemma 4 base adds plain Apache 2.0. Download earns 10, with no gate. Hosted earns 5, as NVIDIA's own API serves it.
Score by category
| Category | Weight this run | LFM3-Audio-2B | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 90 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +10 |
| Risk | 20%20 | 40 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +40 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 52 | 65 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +13 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | LFM3-Audio-2B | NVIDIA Nemotron 3.5 Bolt E4B 4GB |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-10-23 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 4/5 (4) |
Verdicts
LFM3-Audio-2B
A 2B model that listens and speaks on an 8 GB machine, transcribes and translates 19 languages and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, voice chat is English only, Liquid hasn't answered the repository's questions, and there are no safety results or data sources.
NVIDIA Nemotron 3.5 Bolt E4B 4GB
A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.
Before you call either
LFM3-Audio-2B
- Start it with
llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinjaorollama run lfm3-audio - llama.cpp ships examples/realtime_client.py for live speech with this model
- Prefill the assistant turn with an open think tag to let it reason before a text reply
- The context is 32K
- Voice chat is English only, while transcription, synthesis and translation cover 19 languages and locales
- Liquid doesn't recommend it for agentic coding or complex multi-step reasoning
NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Quickest start is
ollama run nemotron-3.5-bolt:e4b, about 8 GB of files, and setnum_ctxto 65536 or less, since NVIDIA supports 64K - With NVIDIA's GGUF use
llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja, and add--reasoning on --reasoning-budget <tokens>to cap thinking - With vLLM 0.31.0 or later pass
--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4and the repository's chat_template.jinja - Pass
enable_thinkingexplicitly inchat_template_kwargs, since the template defaults to off whatever the card says, and setskip_special_tokensfalse as the card's examples do - Use
temperature=1.0andtop_p=0.95in both modes, as the card says - Set
conversational_modetrue for spoken replies, which appends a brevity instruction to the system prompt - Answer a slow tool at once with a pending
toolmessage, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says - Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines
Questions
Which is better for AI agents, LFM3-Audio-2B or NVIDIA Nemotron 3.5 Bolt E4B 4GB?
LFM3-Audio-2B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach, risk and documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/lfm3-audio-2b-vs-nemotron-3-5-bolt-e4b-4gb.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm3-audio-2b.json·/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json - From a terminal
anchor compare lfm3-audio-2b nemotron-3-5-bolt-e4b-4gb(the CLI) - Over MCP
compare_tools {"a": "lfm3-audio-2b", "b": "nemotron-3-5-bolt-e4b-4gb"}at/mcp, no key