Best Local LLMs for 8GB RAM in 2026: Ollama, LM Studio, and Model Picks

Small multimodal assistant

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

Balanced small-device assistant

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

Small general assistant

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

Small English reasoning

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

Low-memory local assistant

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

Tiny local chat

Runs almost everywhere and is useful for local automation tests.

Next-generation Granite evaluation

Track this for Apache-licensed business deployment tests.

Tiny Google open model tests

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

Small enterprise-friendly assistant

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

Tiny reasoning experiments

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

Tiny local chat and quick smoke tests

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

Tiny Hunyuan local tests

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

Local semantic search

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

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.

Enterprise-style local retrieval

A permissive embedding model for private search pipelines.

Classic lightweight local assistant

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

Large general open-weight assistant

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

MoE general assistant

Still useful for local MoE experiments and multilingual workloads.

High-quality local multimodal work

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

Workstation-grade open model

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

Efficient MoE reasoning

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

Updated 24B multimodal assistant

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

Frontier local workstation model

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

Efficient larger multimodal model

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

Edge-friendly multimodal assistant

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

High-quality local assistant

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

Server-class open-weight deployment

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

Long-context multimodal servers

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

Higher-quality local reasoning

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

Efficient multilingual local chat

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

Balanced multimodal local model

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

Local repository edits

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

Default open local assistant

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

Efficient edge multimodal use

Track this for compact visual assistants and quick local prototypes.

Compact English reasoning

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

Large open agentic model

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

Fast workstation multimodal model

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

Very large local AI infrastructure

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

Small coding assistant

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

Open-weight server assistant

Evaluate license and deployment constraints before production use.

Stronger local image chat

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

Local image understanding

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

Efficient GLM deployment

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

Reasoning on common GPUs

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

Newest MiniMax open-weight candidate

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

Small local reasoning

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

Permissive local business model

Good candidate for private enterprise assistants and RAG evaluation.

Updated MiniMax local server track

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

Large open agent model

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

Local visual question answering

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

Cluster-class open agent model

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

Business-grade open model testing

A larger Granite candidate for teams that want an Apache-licensed model as a default.

Mistral local vision assistant

Good for open multimodal experiments with the Mistral ecosystem.

Classic local image chat

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

Small multimodal Microsoft model

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

Transparent open-model evaluation

A good comparison point for fully open training pipelines.

Fully open large-model research

A larger transparent model family entry for reproducible AI research.

Large local infrastructure reference

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

Fully open research baseline

Choose OLMo when training data and model transparency matter.

Open visual reasoning research

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

Large local reasoning servers

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

Serious local reasoning

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

High-quality local coding

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

Large local vision servers

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

Large local visual assistant

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

Better local math and logic

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

Code completion and generation

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

Large local multimodal analysis

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

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.

Agentic coding tasks

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

Large instruction embeddings

Use when retrieval quality matters more than embedding throughput.

Reasoning-focused Phi workflows

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

Large open visual reasoning

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