# OTel-2.0-LLM-31B-IT by Farbod Tavakkoli: Open-Weight Model Source: https://savrn.com/models/otel-2-0-llm-31b-it Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve OTel-2.0-LLM-31B-IT (31.3B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 62.5 GB | 75.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 31.3 GB | 37.5 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 15.6 GB | 18.8 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 22, 2026. [OTel-2.0-LLM-31B-IT on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/otel-2-0-llm-31b-it/gpus) ## SAVRN's Notes on OTel-2.0-LLM-31B-IT A 31.3B-parameter model with a 262,144-token context, post-trained from Gemma 4 31B-IT on roughly 440 billion telecom tokens: OTel-2.0-LLM-31B-IT is built for network operations, standards interpretation, configuration help and retrieval-backed answering, which is why the window matters — the input is a standards document, not a ticket. Memory runs 75.1 GB at 16-bit, 37.5 GB at 8-bit, 18.8 GB at 4-bit, and every row fits one MI300X at $1.85 an hour. The Gemma4ForConditionalGeneration architecture records the base. Apache 2.0 permits commercial deployment, modification and redistribution with notices kept, though the Gemma base's terms deserve their own read. The repository is 39 files and 126.8 GB — about twice the 62.5 GB of 16-bit weights — so confirm what the extra files hold before pulling. The card points evaluators at agentic suites such as SWE-Bench Verified and τ²-Bench with preserved-thinking mode. If your workload is not telecom, the neighboring Qwen3.6-27B offers a more general long-context run. SAVRN Research ## Model Card By Farbod Tavakkoli, published under apache-2.0, revision de9c87a5681a. Versioning notice: This card describes the current OTel 2.0 OSFT checkpoint. The model weights may be updated over time. For reproducible evaluation or production deployment, pin a specific model revision, checkpoint hash, or release tag rather than relying on the floating latest version. OTel-2.0-LLM-31B-IT is a telecom-specialized instruction model post-trained from Gemma 4 31B-IT on approximately 440 billion telecom training tokens. It is the first release in the OTel 2.0 family and is designed to support telco-grade AI workflows across network operations, standards interpretation, product development, network configuration assistance, RAG, and telecom-specific question answering. OTel 2.0 extends the original OTel effort from a RAG-oriented telecom fine-tuning release into a larger domain-adapted training program. The model was trained from a much larger standards and telecom corpus, with new data preparation coverage for direct telecom QnA, abstention, RAG, base-model-style telecom data, and general-purpose instruction-following and tool-calling examples. The current training mixture does not include telecommunications-specific MCP, tool-calling, or instruction-following examples. [Read the full model card (5,000 words)](https://savrn.com/models/otel-2-0-llm-31b-it/card) ## Configuration Architecture Gemma4ForConditionalGeneration Context length (tokens) 262,144 Layers 60 Hidden size 5,376 Feed-forward size 21,504 Attention heads 32 Key/value heads 16 Head dimension 256 Vocabulary size 262,144 Sliding window (tokens) 1,024 Model type gemma4 ## Identity and Version Repository farbodtavakkoli/OTel-2.0-LLM-31B-IT Publisher Farbod Tavakkoli Task Text generation Modality Text Library transformers Parameters 31.3B parameters Languages en Revision de9c87a5681a4d6dce4ea2419eec8a2aadd5e071 First published 2026-07-23 Last updated 2026-10-06 ## Files and Weights 39 files, 126.8 GB in total. The weights are 29 files totalling 126.8 GB in safetensors. Weights29 files · 126.8 GB Configuration5 files · 126.6 KB Tokenizer2 files · 32.2 MB Documentation1 file · 40.3 KB Other1 file · 19.3 KB Repository1 file · 2.1 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model-00001-of-00014.safetensors | Weights | 2.8 GB | 2d8a6ceb85e0 | | model-00001-of-00015.safetensors | Weights | 2.8 GB | 05d6cc31f1d4 | | model-00002-of-00014.safetensors | Weights | 5.0 GB | 0572c4159bd0 | | model-00002-of-00015.safetensors | Weights | 2.2 GB | d436d321c12c | | model-00003-of-00014.safetensors | Weights | 4.9 GB | e8ff2c96a762 | | model-00003-of-00015.safetensors | Weights | 4.9 GB | d2a0ecb1317a | | model-00004-of-00014.safetensors | Weights | 4.9 GB | 196f06858c74 | | model-00004-of-00015.safetensors | Weights | 4.9 GB | 3f708ec3ae58 | | model-00005-of-00014.safetensors | Weights | 4.9 GB | 83cf22e97659 | | model-00005-of-00015.safetensors | Weights | 4.9 GB | bc3a11f4b53a | | model-00006-of-00014.safetensors | Weights | 4.8 GB | cce5e5e46a5a | | model-00006-of-00015.safetensors | Weights | 4.8 GB | ecb8e0e7c4b4 | | model-00007-of-00014.safetensors | Weights | 4.9 GB | b6be3d84c71d | | model-00007-of-00015.safetensors | Weights | 4.9 GB | e0d8dd8f5e11 | | model-00008-of-00014.safetensors | Weights | 4.9 GB | 70af83af358a | | model-00008-of-00015.safetensors | Weights | 4.9 GB | 120ec437e88d | | model-00009-of-00014.safetensors | Weights | 4.9 GB | 16f5cac9f9dd | | model-00009-of-00015.safetensors | Weights | 4.9 GB | ede9fbe726b3 | | model-00010-of-00014.safetensors | Weights | 4.9 GB | 9e75adcd906a | | model-00010-of-00015.safetensors | Weights | 4.9 GB | cbcb624d28f9 | | model-00011-of-00014.safetensors | Weights | 4.9 GB | dc0842a77b6c | | model-00011-of-00015.safetensors | Weights | 4.9 GB | 5f4d60a341f7 | | model-00012-of-00014.safetensors | Weights | 4.8 GB | b2deff051ec3 | | model-00012-of-00015.safetensors | Weights | 4.8 GB | 1b9a619e41ba | | model-00013-of-00014.safetensors | Weights | 4.9 GB | 7b0ac88cb864 | | model-00013-of-00015.safetensors | Weights | 4.9 GB | 16ba7c1662a7 | | model-00014-of-00014.safetensors | Weights | 2.7 GB | 6f853a792175 | | model-00014-of-00015.safetensors | Weights | 2.7 GB | 7cd821f4b598 | | model-00015-of-00015.safetensors | Weights | 1.2 GB | 217b3dbd53ad | | config.json | Configuration | 4.4 KB | — | | generation_config.json | Configuration | 208 B | — | | model.safetensors.index.json | Configuration | 120.2 KB | — | | processor_config.json | Configuration | 1.7 KB | — | | special_tokens_map.json | Configuration | 100 B | — | | README.md | Documentation | 40.3 KB | — | | chat_template.jinja | Other | 19.3 KB | — | | .gitattributes | Repository | 2.1 KB | — | | tokenizer.json | Tokenizer | 32.2 MB | cc8d3a0ce364 | | tokenizer_config.json | Tokenizer | 3.7 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 126.8 GB [Download from Farbod Tavakkoli](https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT) Released by Farbod Tavakkoli through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by arXiv:2504.07097 ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 126.8 GB | | 16-bit | 62.5 GB | | 8-bit | 31.3 GB | | 4-bit | 15.6 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Compare OTel-2.0-LLM-31B-IT - [Fastino-Nemotron-3.5-Lightning-Finance vs OTel-2.0-LLM-31B-IT](https://savrn.com/models/compare/fastino-nemotron-3-5-lightning-finance-vs-otel-2-0-llm-31b-it) - [GLM-4.7-Flash vs OTel-2.0-LLM-31B-IT](https://savrn.com/models/compare/glm-4-7-flash-vs-otel-2-0-llm-31b-it) - [NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 vs OTel-2.0-LLM-31B-IT](https://savrn.com/models/compare/nvidia-nemotron-3-nano-30b-a3b-bf16-vs-otel-2-0-llm-31b-it) ## Questions About OTel-2.0-LLM-31B-IT ### How much GPU memory does OTel-2.0-LLM-31B-IT need? About 75.1 GB at 16-bit and 18.8 GB at 4-bit: the weights (31.3B parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run OTel-2.0-LLM-31B-IT on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 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. ### What is OTel-2.0-LLM-31B-IT's context length? 262,144 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Text generation ### [Gemma-4-Writers-31B](https://savrn.com/models/gemma-4-writers-31b) [Ateron](https://savrn.com/model-publishers/ateron) "Less Gemma - more human" I had a lot of fresh and creative experience with both ConicCat tunes, but they had some instability issues, that I hopefully fixed there. 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Terminal Bench, SWE Bench Verified τ^2-Bench For τ^2-Bench evaluation, we added an additional prompt to the Retail and Telecom user interaction to avoid failure modes caused by users ending the interaction… Open weights mit 31.2B parameters 202,752 tokens transformers [View model](https://savrn.com/models/glm-4-7-flash) Model · Text generation ### [glm47-flash-blockwise-fp8](https://savrn.com/models/glm47-flash-blockwise-fp8) [RadixArk](https://savrn.com/model-publishers/radixark) Blockwise FP8 quantization of zai-org/GLM-4.7-Flash: e4m3 weights with one scale per 128×128 block and dynamic activation scaling, about half the size of the BF16 original (30 GiB vs 58 GiB). See the original model card for model details. gsm8k (5-shot, full test set) on SGLang with MTP speculative decoding: 0.809 for FP8 vs 0.819 for BF16; average accept length 2.41 vs 2.43. Attention tensor parallelism must be 1 or 2: at 4, each rank's kvbproj shard (2240 rows) is not a multiple of the 128-row block. For more GPUs, use DP attention with expert parallelism, for example --tp-size 4 --dp-size 4 --enable-dp-attention --ep-size 4 --moe-a2a-backend deepep --cuda-graph-max-bs-decode 128. MIT… Open weights mit 31.2B parameters 202,752 tokens transformers [View model](https://savrn.com/models/glm47-flash-blockwise-fp8) Model · Text generation ### [Qwen3-VL-30B-A3B-Instruct-AWQ](https://savrn.com/models/qwen3-vl-30b-a3b-instruct-awq) [QuantTrio](https://savrn.com/model-publishers/quanttrio) As of 2025-10-08, create a fresh Python environment and run: For more details, refer to vLLM Official Qwen3-VL Guide Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date. This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment. Text Understanding on par with pure LLMs: Seamless text–vision… Open weights apache-2.0 31.1B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-vl-30b-a3b-instruct-awq) Model · Text generation ### [nemotron-30b-a3b-gptq-component-jang-g64-lora-mtp](https://savrn.com/models/nemotron-30b-a3b-gptq-component-jang-g64-lora-mtp) [Roman Romenskyi](https://savrn.com/model-publishers/roman220220) Open weights 31.6B parameters 262,144 tokens mlx [View model](https://savrn.com/models/nemotron-30b-a3b-gptq-component-jang-g64-lora-mtp) ## Farbod Tavakkoli [All models and datasets](https://savrn.com/model-publishers/farbodtavakkoli) ## Versions - [de9c87a5681a](https://savrn.com/models/otel-2-0-llm-31b-it/versions/de9c87a5681a) · current 2026-10-12 - [6d425c390399](https://savrn.com/models/otel-2-0-llm-31b-it/versions/6d425c390399) 2026-10-06 ## Explore More - [All text generation models](https://savrn.com/models/tasks/text-generation) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-07. - [Hugging Face record](https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT) - [How the hub is built](https://savrn.com/model-hub/methodology)