Local & Open-Weight AI Deployment

Browse published open-weight models for self-hosted execution across private cloud, workstation, or on-premise infrastructure.

Local AI Deployment Realities & Considerations

Deploying open-weight models locally offers greater data perimeter isolation and eliminates cloud API rate limits. However, self-hosting requires realistic engineering trade-offs:

Open-Weight vs. Open-Source Open weights permit downloading model parameters, but commercial licensing terms vary. Some open weights restrict commercial deployment above specific active user thresholds.
Hardware & Operating Costs Local deployment replaces per-token API fees with server hardware CAPEX, electricity, cooling, and ongoing system administration overhead.
Memory & Storage Dependence VRAM footprint depends heavily on parameter count, precision (FP16 vs INT8/INT4), and quantization format (GGUF, AWQ, EXL2). Large context windows consume additional KV-cache memory.
Runtime Performance & Maintenance CPU-only execution may suffer high latency. System updates, security patches, vulnerability monitoring, and model versioning become direct operator responsibilities.
Comparing cloud APIs with local execution? Read Hosted vs Local Guide →

8-Step Local Deployment Journey

Step 1

Define Task

Specify exact output requirements, latency targets, and target modalities.

Step 2

Select Size

Choose candidate parameter count (8B, 14B, 32B, 70B) based on target hardware.

Step 3

Check License

Verify commercial redistribution terms and user scaling threshold rules.

Step 4

Choose Runtime

Select runtime engine (vLLM, Ollama, llama.cpp, or TensorRT-LLM).

Step 5

Estimate Memory

Calculate weights VRAM + KV-cache RAM requirements for target context window.

Step 6

Test Quantization

Benchmark precision trade-offs (Q4_K_M vs Q8_0 vs FP16) for perplexity.

Step 7

Measure Latency

Evaluate Time-To-First-Token (TTFT) and Tokens-Per-Second (TPS) under load.

Step 8

Plan Updates

Implement automated health checks, security patches, and version control.

Published Local Deployment Eligible Models (11)

Open-weight model Generally available

Codestral

Provider: Mistral AI

Mistral AI's 22B code-specialized model trained on 80+ programming languages, supporting fill-in-the-middle code completion and a 32k context window.

Classification Basis: Catalog evidence confirms local execution support.
Licensing: Open-weights terms (Varies by provider)
Context Window: 32,768 tokens
Code generation model Model Details →
Open-weight model Generally available

Command R

Provider: Cohere

Cohere's 35B open-weights model designed for cost-effective enterprise RAG, search grounding, and multilingual workflows.

Classification Basis: Catalog evidence confirms local execution support.
Licensing: Open-weights terms (Varies by provider)
Context Window: 128,000 tokens
General language model Model Details →
Open-weight model Generally available

Command R+

Provider: Cohere

Cohere's 104B flagship open-weights model optimized for enterprise RAG workflows, multi-step tool use, and 10 key business languages.

Classification Basis: Catalog evidence confirms local execution support.
Licensing: Open-weights terms (Varies by provider)
Context Window: 128,000 tokens
General language model Model Details →
Open-weight model Generally available

DeepSeek-Coder-V2-Lite

Provider: DeepSeek

DeepSeek's 16B MoE (2.4B active) open-weights code model delivering 128k long-context code completion under the MIT license.

Classification Basis: Catalog evidence confirms local execution support.
Licensing: Open-weights terms (Varies by provider)
Context Window: 128,000 tokens
Code generation model Model Details →
Open-weight model Generally available

DeepSeek-V2.5

Provider: DeepSeek

DeepSeek's 236B Mixture-of-Experts (21B active) open-weights model combining general chat and advanced code generation with a 128k context window.

Classification Basis: Catalog evidence confirms local execution support.
Licensing: Open-weights terms (Varies by provider)
Context Window: 128,000 tokens
General language model Model Details →
Open-weight model Generally available

Llama 3.1 405B

Provider: Meta AI

Meta AI open-weights 405-billion parameter foundation model offering frontier-class reasoning, code generation, and synthetic data synthesis.

Classification Basis: Catalog evidence confirms local execution support.
Licensing: Open-weights terms (Varies by provider)
Context Window: 131,072 tokens
General language model Model Details →
Open-weight model Generally available

Llama 3.1 70B

Provider: Meta AI

Meta AI open-weights 70-billion parameter model balancing high-tier language intelligence with local GPU server deployment feasibility.

Classification Basis: Catalog evidence confirms local execution support.
Licensing: Open-weights terms (Varies by provider)
Context Window: 131,072 tokens
General language model Model Details →
Open-weight model Generally available

Llama 3.1 8B

Provider: Meta AI

Meta's lightweight open-weights foundation model offering an expanded 128k context window and multilingual capabilities for local deployment and edge inference.

Classification Basis: Catalog evidence confirms local execution support.
Licensing: Open-weights terms (Varies by provider)
Context Window: 131,072 tokens
General language model Model Details →
Open-weight model Retired

Mistral Large 2

Provider: Mistral AI

Mistral AI's 123B flagship open-weights model featuring a 128k context window, advanced multilingual reasoning, and enterprise code generation.

Classification Basis: Catalog evidence confirms local execution support.
Licensing: Open-weights terms (Varies by provider)
Context Window: 128,000 tokens
General language model Model Details →
Open-weight model Generally available

Mistral Small 2

Provider: Mistral AI

Mistral AI's 22B Apache 2.0 open-weights model designed for low-latency text generation, precise tool use, and efficient local deployment.

Classification Basis: Catalog evidence confirms local execution support.
Licensing: Open-weights terms (Varies by provider)
Context Window: 128,000 tokens
General language model Model Details →
Open-weight model Generally available

Qwen2.5-Coder-32B-Instruct

Provider: Alibaba Cloud

Alibaba's flagship 32B open-weights code model featuring Apache 2.0 licensing, 128k context, and multi-language programming mastery.

Classification Basis: Catalog evidence confirms local execution support.
Licensing: Open-weights terms (Varies by provider)
Context Window: 131,072 tokens
Code generation model Model Details →

Platform Status

Optix AI is under active development. Verified model records, pricing schemas, deployment guides, and published benchmark data are being added progressively. Our mission is to provide transparent AI model discovery and comparison with clearly sourced facts.

Data Verification: Ongoing curation and manual verification of model specifications against official sources.
Regular Updates: Addition of newly released commercial and open-source models as records are verified.
Developer First: Built with transparency, clearly sourced facts, and community feedback.