Best Local LLM for RTX 4090

Efficient MoE reasoning

Mixture-of-experts design can offer strong quality without activating every parameter.

Workstation-grade open model

A serious local upgrade for coding, agent workflows, and difficult reasoning tasks.

Higher-quality local reasoning

Useful when 8B is not consistent enough and you still want practical local speed.

Serious local reasoning

Use when answer quality matters more than speed and you have workstation memory.

Better local math and logic

A sensible upgrade when 7B and 8B distills are too brittle.

Compact English reasoning

A practical option when you want stronger reasoning than tiny models.

Default open local assistant

Strong everyday pick for multilingual chat, coding, and reasoning on consumer hardware.

Large local multimodal analysis

A workstation-class option for documents, diagrams, UI review, and visual reasoning.

Small local reasoning

Use for math, logic, and step-by-step problem solving on consumer hardware.

Reasoning-focused Phi workflows

Pick this over base Phi-4 when step-by-step problem solving is the main job.

Reasoning on common GPUs

A popular local R1 size for RTX 3060-class hardware.

Efficient GLM deployment

A lighter GLM-family option for local servers and larger workstations.

High-quality local multimodal work

Best reserved for 24 GB GPUs, high-memory Macs, or larger systems.

High-quality local coding

One of the strongest widely used open coding models for local workstations.

Efficient larger multimodal model

Useful when you want a larger Gemma family model without activating every parameter.

Large local visual assistant

A workstation-class LLaVA model for more demanding visual tasks.

Frontier local workstation model

A larger Gemma option for capable desktops and local multimodal workflows.

Small English reasoning

A compact model for local assistants, classification, and structured reasoning prompts.

Balanced multimodal local model

Good on Apple Silicon with enough unified memory or a 12 GB GPU.

Edge-friendly multimodal assistant

Choose this when image input matters and you still want a compact model.

Fast workstation multimodal model

Good on Apple Silicon with enough unified memory or 12 GB plus GPUs.

Balanced small-device assistant

Good first model for 8 GB to 16 GB machines when speed matters.

Tiny reasoning experiments

Good for testing reasoning prompts locally before moving to larger R1 distills.

Fully open large-model research

A larger transparent model family entry for reproducible AI research.

Business-grade open model testing

A larger Granite candidate for teams standardizing on Apache-licensed models.

Permissive local business model

Good candidate for private enterprise assistants and RAG evaluation.

Transparent open-model evaluation

A good comparison point for fully open training pipelines.

Code completion and generation

Use for code-specific workflows; check license terms before commercial deployment.

Local repository edits

A useful middle tier when 7B misses framework details but 32B is too heavy.

Updated 24B multimodal assistant

A newer Small-family option for instruction following, vision, and tool use.

High-quality local assistant

Strong when you need low-latency function calling and multimodal input.

Open visual reasoning research

A transparent vision-language model family for local multimodal tests.

Stronger local image chat

Use this when the 7B model misses details and you have enough memory.

Tiny local chat and quick smoke tests

Runs on almost any laptop, but keep expectations modest for coding and reasoning.

Local image understanding

A practical option for screenshots, visual Q&A, and document images.

Efficient multilingual local chat

A good mid-size Mistral option for broad language coverage.

Agentic coding tasks

A code-agent oriented open model for repository-level work.

Efficient edge multimodal use

Track this for compact visual assistants and quick local prototypes.

Small multimodal Microsoft model

Supports text, vision, and audio-style multimodal workflows depending on runtime support.

Local visual question answering

Use when screenshots, charts, receipts, or documents matter more than pure text speed.

Classic lightweight local assistant

A stable baseline model for general local chat and tool experiments.

Small coding assistant

A practical code-focused model for local autocomplete, review, and refactor prompts.

Classic local image chat

A widely supported visual assistant for screenshots and simple image questions.

Small multimodal assistant

A compact option when image input matters but hardware is limited.

Large instruction embeddings

Use when retrieval quality matters more than embedding throughput.

Low-memory local assistant

A better floor than sub-1B models when you need basic coding help on small devices.

Fully open research baseline

Choose OLMo when training data and model transparency matter.

Mistral local vision assistant

Good for open multimodal experiments with the Mistral ecosystem.

Small general assistant

A lightweight Meta open-weight option for simple local use.

Tiny local chat

Runs almost everywhere and is useful for local automation tests.

Local semantic search

Use for private RAG indexes, document search, and lightweight retrieval.

Tiny Google open model tests

Useful for mobile-adjacent experiments and low-memory chat.

Next-generation Granite evaluation

Track this for Apache-licensed business deployment tests.

Higher-quality local embeddings

Good default when embedding quality matters more than model size.

Multilingual RAG retrieval

A strong embedding pick for multilingual search and hybrid retrieval workflows.

Multilingual document embeddings

Check license terms before commercial usage; useful for local multilingual evaluation.

Small enterprise-friendly assistant

Apache-licensed model for teams that care about permissive licensing.

Enterprise-style local retrieval

A permissive embedding model for private search pipelines.

Tiny Hunyuan local tests

Useful when you want a very small Chinese-English local model.

MoE general assistant

Still useful for local MoE experiments and multilingual workloads.

Large local reasoning servers

Better suited to multi-GPU rigs or high-memory Apple Silicon systems.

Cluster-scale open reasoning

Reference the full model for server planning, not single-desktop installation.

Native multimodal agentic model

A server-scale candidate for teams tracking open agent models.

Server-class open-weight deployment

Track this for cluster or multi-GPU servers rather than normal desktops.

Large open agentic model

Track this for hosted local infrastructure, coding agents, and long-context analysis.

Large open visual reasoning

Use on high-memory systems when smaller vision models lack accuracy.

Large open agent model

A high-capability open model for servers with strong memory budgets.

Cluster-class open agent model

Useful for tracking frontier open-weight systems, but not ordinary local desktops.

Large local vision servers

Use this only on serious multi-GPU or high-memory systems.

Large general open-weight assistant

A common baseline for strong local text performance on large rigs.

Newest MiniMax open-weight candidate

Use as a research and infrastructure planning entry until local runtimes mature.

Updated MiniMax local server track

A large open-weight candidate for organizations with inference clusters.

Long-context multimodal servers

Open-weight Llama 4 model for server-side local deployment planning.

Open-weight server assistant

Evaluate license and deployment constraints before production use.

Very large local AI infrastructure

Treat this as a data-center candidate, not a normal desktop install.

Large local infrastructure reference

Track for server deployments and China-market open model coverage.