Best Omni Multimodal Models for Apple Silicon Macs in 2026

Quick Answer

NVIDIA’s Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 is the top pick as of September 2026 because it is the newest release among the eligible candidates [1][3][4][6]. In order, the ranking is Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 (NVIDIA), Gemma 4 E4B IT (Google), Gemma 4 E2B IT (Google), and MiniCPM-o-4.5 (OpenBMB) [1][3].

Key Takeaways

• Ranking note: scope admits only omni multimodal models with several input and output types, tagged any-to-any on Hugging Face, from labs with a published paper or leaderboard record. [2][5][7] Every candidate has no public benchmark yet; ordering is newest release first, then downloads. [1][3][4][6]
• #1: NVIDIA Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 takes first place because its April 20, 2026 release is the newest among the candidates. [1][3][4][6] Its 33B parameters imply 16.5 GB of 4-bit weights, estimated (params x 0.5 bytes); context is 256K tokens, under the nvidia-open-model-agreement license. [6]
• #2: Google Gemma 4 E4B IT has 8B parameters: 4.0 GB of 4-bit weights, estimated (params x 0.5 bytes), with 128K-token context and Apache-2.0 licensing. [1] Its higher download count breaks the shared release-date tie with Google Gemma 4 E2B IT. [1][3]
• #3: Google Gemma 4 E2B IT has 5.1B parameters: 2.55 GB of 4-bit weights, estimated (params x 0.5 bytes), with 128K-token context and Apache-2.0 licensing. [3] Its estimated weight footprint is smaller than the other candidates’ estimates. [1][3][4][6]
• #4: OpenBMB MiniCPM-o-4.5 has 9.4B parameters: 4.7 GB of 4-bit weights, estimated (params x 0.5 bytes), with 40K-token context and Apache-2.0 licensing. [4] Its February 3, 2026 release places it after the other candidates under the release-date ordering. [1][3][4][6]

How do omni multimodal models compare on memory, context, licenses and published benchmarks?

Which omni multimodal models should you consider for an Apple Silicon Mac?

1. Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16

Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 by NVIDIA ranks first because its Hugging Face publication date of April 20, 2026 is newer than the other eligible releases.[6][1][3][4] The model has 33B parameters, a 256K-token context window and BF16 weights totaling 66.0 GB.[6] NVIDIA distributes the weights under the nvidia-open-model-agreement.[6] The context window makes long-context multimodal workloads its clearest use case on paper; the ranking does not establish a quality or speed advantage.

For Apple Silicon planning, its 33B parameters imply approximately 16.5 GB for 4-bit weights, estimated (params x 0.5 bytes).[6] Treat that figure as a starting point for unified-memory budgeting, with additional capacity needed for execution and context. A specific Mac RAM recommendation would require a runtime-memory estimate beyond the weight calculation. The practical caveat is that Apple Silicon runtime compatibility, quantized availability and throughput are not established here, so the weight estimate alone cannot establish whether the model runs well locally.

Ranking note: the ordering is newest release first, then downloads; Nemotron has no public benchmark yet. The ranking admits only omni multimodal models with several input and output types, using Hugging Face’s any-to-any pipeline tag, from labs with a published paper or leaderboard record.[2][5][7] Selection therefore reflects release recency, while memory, context and license remain the practical deployment considerations.

2. Gemma 4 E4B IT

Gemma 4 E4B IT by Google ranks second under the ordering rule “newest release first, then downloads.”[1][3][6] Its Hugging Face publication date of March 2, 2026, places it behind NVIDIA’s Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16, published April 20, 2026.[1][6] Google’s Gemma 4 E2B IT shares its publication date, so downloads break the tie: 4,384,331 versus 3,114,511 over the last 30 days, as of September 25, 2026.[1][3] The position reflects release timing and adoption; there is no public benchmark yet.

The model has 8B parameters, a 128K-token context limit, and an Apache-2.0 license.[1] Published BF16 weights occupy 16.0 GB.[1] For Apple Silicon hardware planning, 4-bit weight storage is approximately 4.0 GB, estimated (params x 0.5 bytes) from the cited 8B parameters.[1] Treat that figure as a starting point for a Mac’s memory budget: the calculation covers weights alone and does not establish a complete unified-memory requirement or confirm that a particular configuration can run it.

Consider Gemma 4 E4B IT for local omni multimodal experimentation where the published 128K-token context limit and Apache-2.0 licensing suit the project.[1] The caveat is practical: Apple Silicon throughput, runtime compatibility, and memory consumption at that context limit remain unverified here. Its ranking should not be read as a measured Mac performance result.

3. Gemma 4 E2B IT

Gemma 4 E2B IT by Google ranks third under the ordering rule: newest release first, then downloads.[1][3][6] Its Hugging Face publication date is March 2, 2026, matching Google’s Gemma 4 E4B IT; E2B follows on downloads, with 3,114,511 versus 4,384,331 over the preceding 30 days as of September 25, 2026.[1][3] The ranking admits only omni multimodal models with several input and output types, tagged any-to-any on Hugging Face, from labs with a published paper or leaderboard record.

Google lists 5.1B parameters, a 128K-token context window, 10.2 GB of BF16 weights and an Apache-2.0 license.[3] Quantized weight storage is approximately 2.55 GB, estimated (params x 0.5 bytes) from the cited 5.1B parameters.[3] For an Apple Silicon Mac, use that estimate as a starting point for memory planning, not a confirmed unified-memory requirement. A specific Mac configuration cannot be recommended confidently without verified runtime compatibility and total memory measurements.

Consider Gemma 4 E2B IT for local multimodal projects where weight storage and an Apache-2.0 license guide model selection.[3] The caveat is execution uncertainty: the advertised context window does not establish practical context capacity on your Mac. Benchmark status is no public benchmark yet; its position reflects publication date and the download tiebreak, rather than demonstrated quality or Mac performance.

4. MiniCPM-o-4.5

MiniCPM-o-4.5 by OpenBMB ranks fourth because its Hugging Face publication date, February 3, 2026, precedes the other candidates’ release dates.[4][1][3][6] The ordering is newest release first, then downloads.[1][3][4][6] The ranking admits only omni multimodal models with several input and output types, tagged any-to-any on Hugging Face, from labs with a published paper or leaderboard record. MiniCPM-o-4.5 has no public benchmark yet, so its position does not establish a performance gap.

The model has 9.4B parameters, a 40K-token context window and an Apache-2.0 license.[4] Its BF16 weights occupy 18.7 GB.[4] For an Apple Silicon Mac, the weight budget is 4.7 GB, estimated (params x 0.5 bytes) from the cited 9.4B parameters.[4] Treat that figure as a weight-storage calculation rather than a verified total RAM requirement. Choose hardware only after checking the intended runtime’s memory requirements and support for the model’s omni capabilities.

Consider MiniCPM-o-4.5 for local omni application prototyping when Apache-2.0 licensing matters and a 40K-token context meets the workload.[4] The practical caveat is that the weight estimate does not establish how well the model runs on a particular Mac. Validate the complete input-to-output workflow before committing hardware; the ranking provides no measured Apple Silicon speed advantage.

How can you estimate quantized model weight memory from parameter counts?

Estimate quantized model weight memory by multiplying the parameter count by the storage per parameter: for 4-bit weights, use estimated (params x 0.5 bytes).[1][3][4][6] Express the result in decimal gigabytes to compare it with the published weight sizes.

Google’s google/gemma-4-E4B-it has 8B parameters: 4.0 GB estimated (params x 0.5 bytes).[1] Google’s google/gemma-4-E2B-it has 5.1B parameters: 2.55 GB estimated (params x 0.5 bytes).[3] Calculate from the published parameter count rather than inferring a count from the model’s name.

OpenBMB’s openbmb/MiniCPM-o-4_5 has 9.4B parameters: 4.7 GB estimated (params x 0.5 bytes).[4] NVIDIA’s Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 (NVIDIA) has 33B parameters: 16.5 GB estimated (params x 0.5 bytes).[6] Use that full parameter count for the weight calculation.[6]

The nominal 4-bit estimate is about one quarter of BF16 weight storage, because the calculation uses 0.5 rather than 2 bytes per parameter.[1][3][6] For example, the Gemma E4B model’s published BF16 weights occupy 16.0 GB, compared with 4.0 GB estimated (params x 0.5 bytes) from its 8B parameters.[1]

Treat the result as a weight-storage estimate, not a measured download size or a complete application memory requirement. A parameter-only calculation does not establish which Apple Silicon Mac can load the model, sustain its advertised context, or run it well.

Which licenses apply to the ranked omni multimodal models?

The ranked omni multimodal models use either Apache-2.0 or nvidia-open-model-agreement: the Google and OpenBMB entries carry Apache-2.0, while the NVIDIA entry carries nvidia-open-model-agreement.[1][3][4][6]

Google’s google/gemma-4-E4B-it and google/gemma-4-E2B-it both list Apache-2.0.[1][3] License choice therefore does not distinguish the Gemma entries in this ranking.[1][3] For an engineering shortlist, record the license alongside each exact repository name so the selected model and its licensing information stay together.

OpenBMB’s openbmb/MiniCPM-o-4_5 also lists Apache-2.0, matching the license listed for the Google entries.[4][1][3] A shortlist organized by the stated model license would group those entries together. Keep that grouping separate from your assessment of local runtime support and suitability for the intended workload.

NVIDIA’s Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 (NVIDIA) lists nvidia-open-model-agreement.[6] Treat that agreement as a separate licensing review when considering the NVIDIA model. For a deployment decision, read the applicable agreement against your intended use, modification and distribution plans. Keep the exact license name in the deployment record rather than replacing it with a general description such as “open weights.”

What does no public benchmark yet mean for model selection?

“No public benchmark yet” means the candidates lack public leaderboard scores, so their ranking does not establish which delivers better model quality or local performance on an Apple Silicon Mac.[1][3][4][6] Treat the order as a starting point for evaluation, not a measured performance comparison.

Ranking note: this ranking admits only omni multimodal models with several input and output types, tagged any-to-any on Hugging Face, from labs with a published paper or leaderboard record.[1][2][3][4][5][6][7] The ordering basis is newest release first, then downloads; downloads serve only as a tiebreak.[1][3][4][6]

NVIDIA’s Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 takes the first position because its Hugging Face publication date, 2026-04-20, is the newest among the eligible candidates.[1][3][4][6] That placement is not evidence of superior answers or faster inference. Any model-card benchmark results should be labeled self-reported, and scores for an older model cannot establish its replacement’s performance.

For Mac selection, compare weight requirements, context and license before choosing what to evaluate. Google’s Gemma 4 E2B instruction-tuned model has 5.1B parameters: 2.55 GB for 4-bit weights, estimated (params x 0.5 bytes).[3] Its listed context is 128K tokens and its license is Apache-2.0.[3] The weight estimate does not establish total running memory or confirm a particular Mac will fit the model.

Choose a candidate whose requirements suit your constraints, then evaluate your intended inputs and outputs locally. Published specifications alone do not establish response quality, latency or a working Mac deployment.

Frequently Asked Questions

Which omni multimodal model ranks first for Apple Silicon Macs?

NVIDIA’s Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 ranks first because its Hugging Face publication date, April 20, 2026, is the newest among the admitted candidates.[6][1][3][4] All candidates have no public benchmark yet, so first place reflects release order rather than demonstrated Mac performance. Treat the ranking as a shortlist for evaluating memory, context and licensing, rather than a measured speed comparison.

How are the omni multimodal models ranked?

Ranking note: this ranking admits only omni multimodal models (several input and output types) from labs with a published paper or leaderboard record (Hugging Face pipeline tags: any-to-any).[2][5][7] The ordering basis is newest release first, then downloads. The order is NVIDIA’s Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16,[6] Google’s Gemma 4 E4B IT,[1] Google’s Gemma 4 E2B IT,[3] and OpenBMB’s MiniCPM-o 4.5.[4] No established-pick exemption is used.

How much memory would quantized weights need?

For 4-bit weights, estimated (params x 0.5 bytes): Nemotron’s 33B parameters imply 16.5 GB;[6] Gemma E4B’s 8B imply 4.0 GB;[1] Gemma E2B’s 5.1B imply 2.55 GB;[3] and MiniCPM-o’s 9.4B imply 4.7 GB.[4] Each calculation describes weight storage only. None establishes total runtime memory or confirms that a particular Mac can run the complete model.

Which model should I consider for long context?

Nemotron lists a context length of 256K tokens,[6] while both Gemma variants list 128K tokens.[1][3] MiniCPM-o lists 40K tokens.[4] Choose a candidate whose documented context covers your intended workload, but keep context capacity separate from memory estimates. The listed limits do not establish whether your Mac can run a model at its full context.

Do all the models use the same license?

Both Gemma variants and MiniCPM-o list Apache-2.0.[1][3][4] Nemotron lists the NVIDIA Open Model License Agreement, identified as nvidia-open-model-agreement.[6] If your project requires Apache-2.0, the Gemma variants and MiniCPM-o match that stated license requirement.[1][3][4] Review the applicable license before deployment; position in the ranking does not determine whether its terms suit your intended use.

Why does Gemma E4B rank above Gemma E2B?

Both Google models were first published on March 2, 2026,[1][3] so downloads break the release-date tie. Gemma E4B recorded 4,384,331 downloads versus Gemma E2B’s 3,114,511 over the last 30 days as of September 25, 2026.[1][3] Both have no public benchmark yet. The ordering therefore reflects popularity only as a tiebreak, without establishing a quality or Mac performance advantage.

Sources

1. google/gemma-4-E4B-it model card (Hugging Face) — 2026-09-25
2. Gemma 4 Technical Report — 2026-07-02
3. google/gemma-4-E2B-it model card (Hugging Face) — 2026-09-25
4. openbmb/MiniCPM-o-4_5 model card (Hugging Face) — 2026-09-25
5. MiniCPM-V: A GPT-4V Level MLLM on Your Phone — 2024-08-03
6. nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 model card (Hugging Face) — 2026-09-25
7. Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence — 2026-04-27