Best Omni Multimodal Models for a 24 GB GPU in 2026: Gemma 4 12B IT

Quick Answer

Google’s Gemma 4 12B IT is the top pick for a 24 GB GPU as of September 2026 because it is the newest eligible release [1][3][5]. In order, the ranking is Gemma 4 12B IT (Google), Nemotron 3 Nano Omni 30B A3B Reasoning BF16 (NVIDIA), and MiniCPM-o 4.5 (OpenBMB) [1][3].

Key Takeaways

• Ranking note: this ranking admits only omni multimodal models with several input and output types, tagged Hugging Face any-to-any, from labs with a published paper or leaderboard record [1][2][3][4][5][6]. No public benchmark scores any candidate, so the ordering is newest release first, then downloads [1][5][3].
• Google Gemma 4 12B IT ranks first because its publication date, May 23, 2026, is the newest among the candidates [1][5][3]. Its 12B parameters imply 6 GB of 4-bit weights, estimated (params x 0.5 bytes) [1]. Context: 256K tokens; license: Apache-2.0; no public benchmark yet [1].
• NVIDIA Nemotron 3 Nano Omni 30B A3B Reasoning BF16 ranks second, published April 20, 2026 [5]. Its 33B parameters imply 16.5 GB of 4-bit weights, estimated (params x 0.5 bytes) [5]. Context: 256K tokens; license: nvidia-open-model-agreement; no public benchmark yet [5].
• OpenBMB MiniCPM-o 4.5 ranks third, published February 3, 2026 [3]. Its 9.4B parameters imply 4.7 GB of 4-bit weights, estimated (params x 0.5 bytes) [3]. Context: 40K tokens; license: Apache-2.0; no public benchmark yet [3].

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

Which omni multimodal models should you consider for a local GPU?

1. Gemma 4 12B IT

Gemma 4 12B IT by Google ranks first because its Hugging Face publication date of May 23, 2026, is the newest among the eligible candidates.[1][3][5] Ranking note: eligibility is limited to 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][4][6] With no public benchmark scoring these candidates, the ordering is newest release first, then downloads.

Gemma 4 12B IT has 12B parameters, a 256K-token context window and an Apache-2.0 license.[1] Published BF16 weights occupy 23.9 GB.[1] Quantized weight storage would be approximately 6 GB at 4-bit precision, estimated (params x 0.5 bytes) from its 12B parameters.[1] A 24 GB GPU is therefore a plausible local target for quantized deployment—estimated (params x 0.5 bytes) for weights, rather than measured total runtime memory.[1]

Choose Gemma for local omni applications where the advertised context window and Apache-2.0 licensing match your requirements.[1] The caveat is that weight storage alone does not establish whether the full 256K-token context fits on your hardware.[1] Treat the memory calculation as a starting point for deployment planning, not a validated operating configuration. Gemma has no public benchmark yet in this comparison, so its position reflects release order rather than demonstrated superiority in quality or speed.

2. Nemotron 3 Nano Omni 30B A3B Reasoning BF16

Nemotron 3 Nano Omni 30B A3B Reasoning BF16 by NVIDIA ranks second under the ordering rule of newest release first, then downloads.[1][3][5] Its April 20, 2026 publication date places it between the other candidates; the position does not establish a performance advantage.[1][3][5] The model has no public benchmark yet. 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.

NVIDIA lists 33B parameters, a 256K-token context window, and 66.0 GB of BF16 weights under the nvidia-open-model-agreement license.[5] Quantized weight storage would be 16.5 GB, estimated (params x 0.5 bytes) from the cited 33B parameters.[5] A 24 GB GPU is therefore a potential local target for quantized weights—estimated (params x 0.5 bytes), not a measured runtime fit.[5] The supplied specifications do not establish total runtime memory requirements or a system RAM recommendation.

Consider the model for local omni workflows where its advertised 256K-token context is relevant.[5] Treat that context limit as a specification, rather than a demonstrated operating point on the target hardware. The practical caveat is that the weight estimate alone cannot establish usable context length or speed; neither has a published measurement here.

3. MiniCPM-o 4.5

MiniCPM-o 4.5 by OpenBMB[3] ranks third because its February 3, 2026 release[3] predates the other eligible releases[1][5]; no public benchmark yet establishes its relative performance. 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. The ordering basis is newest release first, then downloads, so its position does not establish a quality disadvantage.

MiniCPM-o 4.5 has 9.4B parameters, a 40K-token context window and an Apache-2.0 license[3]. Its BF16 weights occupy 18.7 GB[3]. For those 9.4B parameters[3], 4-bit weight memory is 4.7 GB, estimated (params x 0.5 bytes)[3]. That weight-only estimate makes quantized local experimentation a practical use to consider, particularly when permissive licensing matters. The published context limit provides a concrete boundary for planning workloads, rather than a guarantee that every configuration can use the full window.

For local hardware planning, a 24 GB GPU appears sufficient for the estimated 4.7 GB weight payload, estimated (params x 0.5 bytes) from the cited parameter count[3]. The caveat is that this calculation covers weights alone: complete runtime memory, host RAM requirements and throughput are not established here. Treat the configuration as a deployment candidate requiring validation, with no public benchmark yet to justify a performance claim.

How do you estimate quantized weight memory from parameter counts?

Estimate quantized weight memory by multiplying the model’s parameter count by 0.5 bytes per parameter for 4-bit weights; label the result “estimated (params x 0.5 bytes).”[1][3][5] Treat the result as a weight-storage calculation, not a measurement of GPU memory during inference.

Google’s google/gemma-4-12B-it has 12B parameters:[1] quantized weight memory is 6 GB, estimated (params x 0.5 bytes).[1] OpenBMB’s openbmb/MiniCPM-o-4_5 has 9.4B parameters:[3] quantized weight memory is 4.7 GB, estimated (params x 0.5 bytes).[3]

NVIDIA’s nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 has 33B parameters:[5] quantized weight memory is 16.5 GB, estimated (params x 0.5 bytes).[5] Use the stated parameter count for the calculation rather than treating numbers embedded in the model name as the weight inventory.

For a storage cross-check, the published BF16 weights are 23.9 GB for Google’s model,[1] 18.7 GB for OpenBMB’s model,[3] and 66.0 GB for NVIDIA’s model.[5] The estimated 4-bit weight payload is approximately one quarter of BF16 storage, using 0.5 rather than 2 bytes per parameter.[1][3][5]

Keep the estimate separate from a fit claim. A parameter-only calculation does not budget for quantization metadata, runtime allocations or the context cache. Before choosing a model for local deployment, check the actual quantized artifact and measure peak GPU memory with the intended runtime, context and inputs.

Does estimated weight memory establish whether a model fits on your GPU?

No. Estimated weight memory covers the weights alone; it does not establish whether a model fits on your GPU during use. Treat the calculation as an initial screening step, not a measurement of total memory consumption or a guarantee that your intended workload will run.

Google’s google/gemma-4-12B-it has 12B parameters, giving estimated weight memory of 6 GB at 4-bit precision, estimated (params x 0.5 bytes).[1] Its listed context length is 256K tokens; that specification does not establish GPU memory requirements at that context length.[1]

NVIDIA’s nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 has 33B parameters, giving estimated weight memory of 16.5 GB at 4-bit precision, estimated (params x 0.5 bytes).[5] Use the published parameter count for this calculation rather than inferring weight memory from the model’s name. Its listed context length is 256K tokens.[5]

OpenBMB’s openbmb/MiniCPM-o-4_5 has 9.4B parameters, giving estimated weight memory of 4.7 GB at 4-bit precision, estimated (params x 0.5 bytes).[3] Its listed context length is 40K tokens.[3] Neither figure establishes total memory consumption for a particular workload.

For a deployment decision, check total GPU memory use with your intended runtime, quantization, input types and context length. Successful weight loading is only an initial check: verify that the model can process representative inputs and complete generation before calling it a fit.

How should you rank omni multimodal models with no public benchmark yet?

Rank models with no public benchmark yet by newest release first, then downloads; the order reflects release timing, not demonstrated performance.[1][5][3]

Google’s google/gemma-4-12B-it ranks first because its publication date, May 23, 2026, is the newest among the eligible candidates.[1][5][3] Status: no public benchmark yet. Its 12B parameters imply 6 GB of 4-bit weights, estimated (params x 0.5 bytes).[1] The listed context is 256K tokens, and the license is Apache-2.0.[1]

NVIDIA’s nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 ranks second, with an April 20, 2026 publication date.[5] Status: no public benchmark yet. Its 33B parameters imply 16.5 GB of 4-bit weights, estimated (params x 0.5 bytes).[5] The listed context is 256K tokens, and the license is nvidia-open-model-agreement.[5]

OpenBMB’s openbmb/MiniCPM-o-4_5 ranks third, with a February 3, 2026 publication date.[3] Status: no public benchmark yet. Its 9.4B parameters imply 4.7 GB of 4-bit weights, estimated (params x 0.5 bytes).[3] The listed context is 40K tokens, and the license is Apache-2.0.[3]

Ranking note: the ordering basis is newest release first, then downloads; downloads serve only as a tiebreaker.[1][5][3] Scope admits only omni multimodal models with several input and output types, tagged Hugging Face any-to-any, from labs with a published paper or leaderboard record.[1][2][3][4][5][6] Treat the weight estimates as planning figures, not verified GPU-fit results, and the listed context lengths as specifications rather than demonstrated local operating points.

Frequently Asked Questions

Which model takes the first position?

Google’s Gemma 4 12B IT (google/gemma-4-12B-it) ranks first because its publication date is the newest among the eligible candidates, with no public benchmark yet to establish a performance winner.[1][3][5] Published on May 23, 2026, the model has 12B parameters, a listed context length of 256K tokens and an Apache-2.0 license.[1] The first position reflects release order, not demonstrated superiority in local inference.

What determines the ranking order?

Ranking note: newest release first, then downloads.[1][3][5] The order is Google’s Gemma 4 12B IT,[1] NVIDIA’s Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16,[5] then OpenBMB’s MiniCPM-o-4_5.[3] Scope: 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] Downloads serve only as a tiebreak; no established-pick exemption is used.

How much memory would quantized weights need?

Weight memory at 4-bit is 6 GB estimated (params x 0.5 bytes) for Gemma’s 12B parameters,[1] 16.5 GB estimated (params x 0.5 bytes) for Nemotron’s 33B parameters,[5] and 4.7 GB estimated (params x 0.5 bytes) for MiniCPM-o’s 9.4B parameters.[3] Those calculations cover weights alone. A complete local run fitting your GPU remains unverified; the estimates do not establish runtime memory use or inference speed.

Which models offer a longer context window?

Gemma and Nemotron each list a context length of 256K tokens,[1][5] while MiniCPM-o lists 40K tokens.[3] Gemma and Nemotron therefore share the longer listed context window in this comparison.[1][3][5] Treat those specifications as model context limits, rather than proof that your local configuration can run at the full listed length. The weight-memory estimates do not establish a usable context budget.

What licenses apply to these models?

Gemma uses Apache-2.0,[1] and MiniCPM-o also uses Apache-2.0.[3] Nemotron uses the NVIDIA Open Model Agreement, listed as nvidia-open-model-agreement.[5] For deployment planning, record the license alongside the exact model repository you select. Open weights should not be treated as a shared license category: the NVIDIA entry has a different agreement from the Google and OpenBMB entries.[1][3][5]

Do published benchmarks show which model runs better locally?

Each candidate has no public benchmark yet in this comparison, so there is no scored basis for declaring a local performance winner.[1][3][5] Publication dates determine the ranking before downloads are considered.[1][3][5] Parameter counts, listed context lengths and calculated weight memory describe different deployment considerations; none supplies a measured inference-speed result. The ranking therefore supports an evaluation shortlist, rather than a claim of verified local performance.

Sources

1. google/gemma-4-12B-it model card (Hugging Face) — 2026-09-25
2. Gemma 4 Technical Report — 2026-07-02
3. openbmb/MiniCPM-o-4_5 model card (Hugging Face) — 2026-09-25
4. MiniCPM-V: A GPT-4V Level MLLM on Your Phone — 2024-08-03
5. nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 model card (Hugging Face) — 2026-09-25
6. Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence — 2026-04-27