Open-Weight LLM · Private & Custom AI
sarvam-105b
A 105B-parameter MoE model optimized for reasoning, agentic tasks, and 22 Indian languages—designed for enterprises building private AI systems that require strong logic and multi-lingual operational intelligence without vendor lock-in.
Sarvam-105B is an open-weight Mixture-of-Experts model with 10.3B active parameters, Apache-licensed, and engineered for complex reasoning, coding, and agentic workflows. For ops teams building self-hosted AI, it offers state-of-the-art performance across knowledge work, math, and language tasks—with particular strength in Indian languages—while staying fully under your control.
Model facts
Private deployment
Run sarvam-105b in your own environment
Self-hosting is straightforward: the model loads via HuggingFace Transformers, vLLM (with a custom fork or hot-patch), or SGLang. Deployment requires GPU capacity (see hardware estimates below) and your own infrastructure. The Apache-2.0 license permits this explicitly. By running Sarvam-105B on your own hardware/cloud, all input data, outputs, and fine-tuning remain within your environment—no third-party model calls, no inference logs sent elsewhere. This is an architecture choice that eliminates API dependencies for sensitive operational work.
Operational AI use cases
Intelligent Support Agent & Ticket Routing
Deploy as a private support chatbot: classify incoming tickets, route to departments, generate draft responses to common issues. The model's strong reasoning and agentic capabilities mean it can handle multi-step troubleshooting (e.g., software debugging, account recovery flows). All customer inquiries stay in your environment; no data leaves for external LLM inference.
Financial & Compliance Document Analysis
Automate parsing of invoices, contracts, and regulatory documents. Sarvam-105B excels at instruction-following (IF Eval: 84.8) and knowledge tasks (MMLU: 90.6), so it can extract key terms, flag risks, and summarize obligations. Self-host to keep financial data on-premise, meeting data residency and audit requirements.
Multilingual Ops Workflows (India-Focused Teams)
For teams operating across India, automate workflows in 22 Indian languages (Hindi, Bengali, Tamil, Telugu, Marathi, etc.). Use for translating internal comms, summarizing regional compliance docs, or fielding support in local languages. The model's state-of-the-art Indian-language performance (by size) means fewer failures and better UX in operational contexts.
Custom AI
As a base for custom AI
Sarvam-105B is a strong base for custom AI products: fine-tune on proprietary datasets (e.g., internal playbooks, domain taxonomies) to create specialized reasoning engines. Its MoE architecture keeps inference cost manageable even at scale. Use it to build white-label AI features (copilots, agents, knowledge systems) that run on your infrastructure—you control the model weights, training data, and outputs entirely.
In the operating system
Where it fits
In an AI operating system, Sarvam-105B serves as the reasoning and agentic engine: handle complex knowledge queries, multi-step planning, and tool-calling workflows. Sit it behind a workflow orchestrator (for routing, guardrails, human-in-the-loop) and connect it to your ops data layer (CRM, ERP, document stores). Use smaller models (or retrieval) for simple classification/tagging; route complex reasoning to Sarvam-105B.
Data control & security
Self-hosting on your infrastructure means data does not transit external APIs. Customer input, model outputs, and training data remain in your environment—valuable for HIPAA, GDPR, or customer-sensitive workflows. However, self-hosting is not security by itself: you are responsible for network isolation, access controls, encryption at rest, and model monitoring. The model itself carries no built-in security features; security is a deployment and operational practice.
Hardware footprint
Estimated VRAM by precision (assuming MoE with 10.3B active): bfloat16 ~22–26 GB (single GPU, no quantization); int8 ~12–15 GB; GPTQ/AWQ 4-bit ~6–8 GB. These are approximations; actual footprint depends on batch size, context length, and backend optimization (vLLM/SGLang reduce overhead via KV-cache and tensor parallelism). For production, plan for 4–8 GPUs (H100/A100 class) for reasonable throughput.
Integration
Integrate via HuggingFace Transformers (standard Python), vLLM (batch/streaming inference with KV-cache optimization), or SGLang (structured generation, better for complex prompts). Sarvam-105B supports chat templates and thinking modes—use these for agentic workflows. Expose inference via a simple FastAPI or OpenAI-compatible endpoint so ops tools (Zapier, Make, internal dashboards) can call it. Requires custom code; not plug-and-play with off-the-shelf SaaS.
When it's not the right fit
- —Context length is not publicly specified in the model card—if you need guaranteed long-context (e.g., 128K token pipelines), test thoroughly or contact developers.
- —Real-time low-latency inference under 100ms: 105B parameters + MoE routing adds per-token overhead; consider quantization or a smaller model (Sarvam-30B) for ultra-low-latency ops.
- —Your ops team has no ML/DevOps expertise: self-hosting requires infrastructure setup, GPU management, and monitoring; this is not a managed service.
- —You need non-English performance at frontier quality: while Indian languages are strong, non-Indian languages and multilingual reasoning are not highlighted as differentiators.
Alternatives to consider
Qwen3-Next-80B-A3B-Thinking
Dense 80B model with competitive math/reasoning; no MoE complexity, but higher per-token cost and requires more VRAM. Good if you prefer architectural simplicity over efficiency.
Mistral-Large (open-weight variant)
Smaller, Apache-licensed, easier to deploy on modest hardware; trades reasoning depth for simplicity. Better for lightweight ops tasks; not as strong on math/agentic work.
LLaMA 3.1 405B (quantized)
Frontier-class open model; requires significant infrastructure but offers max generality. Choose if you need broader language coverage or maximal reasoning; overkill for most ops.
Related open models
FAQ
Can I run Sarvam-105B entirely on-prem without any cloud or API calls?
Yes. Deploy on your own GPU infrastructure (or rent bare-metal GPU cloud). The model card and Apache license permit it. Use vLLM or SGLang for inference. All data stays in your environment.
What is the commercial use status?
Apache-2.0 is permissive: you can use Sarvam-105B commercially, fine-tune it, and build products on top—as long as you include the license notice and attribute the original model. No restrictions on revenue or deployment model.
How do I know if Sarvam-105B is better than a smaller model for my ops task?
Run a PoC: benchmark on your actual data (e.g., support tickets, documents). Sarvam-105B excels at reasoning and multi-step tasks; if your workload is simple classification or retrieval, a 7B/13B model may suffice and save compute cost.
Is there a risk that Sarvam goes out of business and the model disappears?
Once downloaded, the model weights are yours to keep and run. Apache-2.0 is perpetual. However, future updates and support depend on Sarvam maintaining the model. Treat it as you would any open-source project: fork, version control, and test thoroughly before production.
Build Your Private AI Operating System
Sarvam-105B is a powerful foundation for self-hosted AI. LLM.co helps you integrate open models into custom ops workflows, fine-tune on your data, and deploy at scale without vendor lock-in. Start a PoC today.