Open-Weight LLM · Private & Custom AI
sarvam-30b
A 30B MoE model optimized for private deployment in resource-constrained ops environments, with strong multilingual (22 Indian languages) and tool-use capability for enterprise automation.
Sarvam-30B is a Mixture-of-Experts model with only 2.4B active parameters, designed for throughput and memory efficiency while maintaining competitive reasoning and coding performance. For ops teams, it's a self-hostable alternative that combines reliable tool calling, multilingual support, and a lean runtime footprint—making it practical for cost-sensitive or data-sensitive internal deployments.
Model facts
Private deployment
Run sarvam-30b in your own environment
Self-hosting Sarvam-30B keeps inference and fine-tuning entirely in your environment. At 2.4B active parameters, it runs on modest GPU hardware (see hardware estimates below). You control data flow, avoid third-party API calls, and maintain compliance boundaries. The model supports vLLM and SGLang for production inference scaling; custom_code=true means you'll verify and manage the code path yourself.
Operational AI use cases
Internal support ticket classification & routing
Deploy Sarvam-30B to automatically sort incoming support tickets by severity, category, and language (support for Hindi, Tamil, Telugu, Marathi, etc. reduces need for separate models). Run inference in-house so ticket content never leaves your network. Tool-use capability enables the model to trigger routing decisions directly to Jira or internal ticketing systems.
Multilingual knowledge base Q&A for distributed teams
Index internal docs (policies, procedures, FAQs) and use Sarvam-30B as a conversational layer. Its 22-language support makes it ideal for companies with offices in India or serving Indian markets. Self-hosted means search queries stay private; no cloud indexing or third-party retention.
Structured data extraction & workflow automation
Use tool calling to extract entities from unstructured logs, emails, or forms—then trigger downstream RPA or API actions (e.g., create a record in your ERP, post to Slack). The model's strong coding performance (HumanEval 92.1%) supports reliable JSON/structured output generation for operational pipelines.
Custom AI
As a base for custom AI
Sarvam-30B is a strong foundation for custom AI products targeting cost-aware or data-sensitive segments. Its MoE architecture allows efficient fine-tuning (only active experts change), and the model card includes chat templates and generation configs ready for integration. You can fine-tune on proprietary domain data (e.g., vertical-specific support, medical/legal text in Indian languages) without shipping data to a vendor.
In the operating system
Where it fits
In an AI OS, Sarvam-30B occupies the **agent & workflow layer**: reliable enough for tool use, reasoning, and multi-step operational tasks (not just retrieval). Its multilingual and coding prowess make it suitable as a **backbone for internal knowledge agents** (RAG + structured automation) and **ops-automation logic**, below or instead of heavier models. For pure retrieval, you'd pair it with a lightweight embedding model in your stack.
Data control & security
Self-hosting Sarvam-30B ensures customer data, proprietary documents, and operational logs remain in your infrastructure—no third-party inference, no model training on your data, no API-level data retention. This is an **architecture choice**, not a guarantee the model itself is 'secure': you are responsible for network isolation, access controls, and prompt injection mitigation. Apache 2.0 licensing permits private use; commercial/compliance review is your own obligation.
Hardware footprint
**Estimate—verify against your hardware.** At 32.15B parameters (MoE, 2.4B active): - **bfloat16 (recommended)**: ~65–80 GB for model weights + KV cache (single-batch inference). Realistic: single A100 (80GB) or dual A40 (2×48GB, tensor-parallel). - **int8 quantization**: ~40–50 GB. - **int4 quantization**: ~20–25 GB (with slight quality loss). MoE routing adds 5–10% overhead. For agentic use (long context, batch size >1), allocate headroom.
Integration
Sarvam-30B integrates via standard HuggingFace Transformers (trust_remote_code=True required). Production inference: use vLLM (PR pending native support; hot-patch available) or SGLang for batching and LoRA serving. Chat template and thinking tokens are supported. For ops workflows: wrap inference in a FastAPI/async handler, connect to your ticketing/ERP APIs via tool schemas (function calling), and run on dedicated GPU hardware or on-prem. Monitor token usage and active expert load to right-size your compute.
When it's not the right fit
- —Context length unknown: no published max_seq_len, so long-document RAG or extended chat histories may require empirical testing or truncation.
- —Agentic benchmarks lag: BrowseComp 35.5%, SWE Bench 34% vs. larger or specialized models—fine for internal automation, risky for customer-facing agent products.
- —Sparse adoption path: smaller download count (55k) and newer architecture mean fewer third-party integrations, fewer finetuned variants, and less community troubleshooting compared to Llama/Mistral.
- —Custom code required: model card flags custom_code=true; you must audit and trust the router/MoE implementations before production.
Alternatives to consider
Mistral-7B or Mistral-Large
Simpler architecture, much broader ecosystem, lower hardware bar. Trade-off: no multilingual focus, no MoE efficiency, less reasoning on math/AIME.
Llama 3.1 (70B, quantized)
Larger context window, strong instruction-following, broader community. Trade-off: higher memory cost even with quantization; less efficient for resource-constrained ops.
OLMo 3.1 32B
Dense, similar size, open training data, strong MMLU. Trade-off: no MoE efficiency, no multilingual specialization, fewer tool-use examples in literature.
Related open models
FAQ
Can I fine-tune Sarvam-30B on proprietary data and keep it private?
Yes. Apache 2.0 permits derivative works. Self-hosted fine-tuning keeps your data in-house. The MoE structure allows LoRA or parameter-efficient fine-tuning on single experts, reducing training overhead. You own the weights and outputs; no data is shared with Sarvam AI or third parties.
Is Sarvam-30B suitable for production internal automation?
Yes, for ops workflows (ticketing, routing, structured extraction, knowledge Q&A). Ensure you verify the custom_code path, run A/B tests on real workloads, and monitor token/inference latency. Agentic benchmarks are moderate, so avoid high-stakes, customer-facing agents without additional validation.
What is the context length, and can I use it for long documents?
Context length is not published in the model card. The architecture mentions 'extremely high rope_theta (8e6)' for long-context stability, but you must test empirically. Start conservative (e.g., 8k tokens) and verify quality; this is a gap to clarify with Sarvam AI or infer from community finetuning.
Can we use Sarvam-30B commercially in our product?
Apache 2.0 permits commercial use, including in closed-source products and SaaS. You must retain the license and copyright notice. No restrictions on redistribution or modifications for internal or external use—but review your specific jurisdiction's compliance and data-handling obligations.
Build Private AI Workflows with Sarvam-30B
Ready to deploy a self-hosted LLM for your ops team? LLM.co helps you integrate Sarvam-30B (or similar open-weight models) into custom automation systems, knowledge agents, and internal tools—with your data always under your control. Let's talk.