SAVRN Model Hub · Comparisons
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 vs OTel-2.0-LLM-31B-IT
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 has 31.6B parameters and OTel-2.0-LLM-31B-IT has 31.3B parameters; NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 is released under other and OTel-2.0-LLM-31B-IT under Apache License 2.0; at 16-bit, NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 needs about 75.8 GB (1x MI300X from $1.85 an hour) and OTel-2.0-LLM-31B-IT about 75.1 GB (1x MI300X from $1.85 an hour).
| Field | NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 | OTel-2.0-LLM-31B-IT farbodtavakkoli/OTel-2.0-LLM-31B-IT |
|---|---|---|
| Publisher | NVIDIA | Farbod Tavakkoli |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 31.6B parameters | 31.3B parameters |
| Architecture | NemotronHForCausalLM | Gemma4ForConditionalGeneration |
| Library | transformers | transformers |
| Context length | 262,144 tokens | 262,144 tokens |
| Repository size | 63.2 GB | 126.8 GB |
| Artifact formats | safetensors, pytorch | safetensors |
| License | other | apache-2.0 |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 75.8 GB | 75.1 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 18.9 GB | 18.8 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | bf77c3174f68 | 6d425c390399 |
| Downloads reported by the hub | 704.5k | 7.2M |
| Last observed | 2026-09-18 | 2026-09-18 |
An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.
Other Reported Results
These results are listed for each model on its own, because the conditions needed to compare them are not stated or do not match. Two results that leave a condition blank are not assumed to share it.
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| LiquidAI/ifstruct-v1.0 | Task ifstruct_v1Metric ifstruct_v1Comparison conditions not established | 86.8 | Liquid AI — IFStruct v1.0 blog (Nemotron-3-Nano-30B-A3B) Reported by a third party |
Evaluated revision not stated | 2026-06-30 |
SAVRN's Notes on NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
Seventy-six gigabytes is the number that decides the hardware here. At 16-bit the 31.6B parameters need 75.8 GB, which fits on a single MI300X with 192 GB at $1.85 per hour, the cheapest setup the Index prices, with room left for the 262,144-token context. At 8-bit the need drops to 37.9 GB and at 4-bit to 18.9 GB, and all 128 routed experts are in that bill. NVIDIA trained it for reasoning and non-reasoning work: it writes a reasoning trace before its answer, and a chat template flag turns the trace off.
The license field says other and we have no summary of its terms on file, so read NVIDIA's license in full before any commercial deployment. Two more checks: the one reported score, 86.8 on Liquid AI's IFStruct v1.0, is third-party reported, and pre-training data stops at June 25, 2025. No Index host serves it by the token yet.
SAVRN's Notes on OTel-2.0-LLM-31B-IT
Post-trained from Gemma 4 31B-IT on roughly 440 billion telecom tokens, OTel-2.0-LLM-31B-IT is aimed at network operations, standards interpretation, configuration help and retrieval-backed answering. Its 31.3B parameters need 75.1 GB at 16-bit, 37.5 GB at 8-bit and 18.8 GB at 4-bit, and all three fit the 192 GB of one MI300X, the cheapest Index setup at $1.85 an hour. The 262,144-token context matters when the input is a standards document rather than a ticket.
Apache 2.0 allows commercial deployment, modification and redistribution, with the notice and change-statement duties kept. Two things to verify. The repository is 39 files and 126.8 GB, about twice the 62.5 GB of 16-bit weights, so confirm what those files hold before pulling them. And because Farbod Tavakkoli post-trained this from a Gemma 4 base, confirm the terms that travel with the base weights fit your plan; no Index host prices are listed.
Questions
Which is larger, NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 or OTel-2.0-LLM-31B-IT?
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 (31.6B parameters) is larger than OTel-2.0-LLM-31B-IT (31.3B parameters), by the parameter counts their publishers report.
Which is cheaper to run, NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 or OTel-2.0-LLM-31B-IT?
At 4-bit, NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 fits on 1x MI300X from $1.85 an hour and OTel-2.0-LLM-31B-IT on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use OTel-2.0-LLM-31B-IT commercially?
Yes. OTel-2.0-LLM-31B-IT is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.