LLM·Dex
Use caseTop 7 picks

Best Local LLMs in 2026

Models you can run entirely on your own machine.

Updated

How we ranked

  • Performance after 4-bit quantization
  • Memory footprint at int4 / int8
  • Inference speed on Apple Silicon and consumer GPUs
  • Tooling support (Ollama, LM Studio, llama.cpp)
  • License permits unlimited local use

Read the full methodology for our sourcing and ranking standards.

"Local" means different things at different hardware tiers. On a 16GB MacBook, you're picking from 7-8B models. On a 24GB consumer GPU, 13-14B fits. On a server with 80GB, the 70B+ tier opens up. DeepSeek-V3 (671B MoE) needs serious hardware to run locally but performs at the frontier.

Llama-4 and Qwen-2.5 are the most popular local picks because of tooling, Ollama, LM Studio, and llama.cpp all have first-class support. Phi-4 is the highest-quality-per-byte pick for resource-constrained setups.

For agentic workloads, Llama-4-70B with vLLM is the practical floor. Below that, function-calling reliability drops fast.

The ranking

  1. #1MetaOpen weights

    Llama 4 8B

    Meta's small Llama 4, built for on-device and edge inference.

    Context
    128K tokens
    Output · 1M
    Pricing not published
    Modalities
    text

    Why it ranks here. Runs on consumer laptops. Broad tooling support. Tracked weakness: Quality limited by size.

  2. #2MetaOpen weights

    Llama 4 70B

    Meta's mid-tier Llama 4, the practical workhorse for self-hosted deployments.

    Context
    128K tokens
    Output · 1M
    Pricing not published
    Modalities
    text, vision

    Why it ranks here. Self-hostable on commodity hardware. Strong all-rounder. Tracked weakness: Custom license.

  3. #3AlibabaOpen weights

    Qwen2.5-72B

    The previous-generation Qwen flagship, still widely deployed for stability.

    Context
    128K tokens
    Output · 1M
    Pricing not published
    Modalities
    text

    Why it ranks here. Mature deployment. Apache-2.0. Tracked weakness: Superseded by Qwen3 for new builds.

  4. #4AlibabaOpen weights

    Qwen2.5-7B

    Small Qwen, practical default for laptop and edge inference.

    Context
    128K tokens
    Output · 1M
    Pricing not published
    Modalities
    text

    Why it ranks here. Apache-2.0. Runs on laptops. Tracked weakness: Quality limited by size.

  5. #5MicrosoftOpen weights

    Phi-4

    Microsoft's 14B model, exceptional quality-per-parameter via curated synthetic training data.

    Context
    16K tokens
    Output · 1M
    Pricing not published
    Modalities
    text

    Why it ranks here. Exceptional quality at 14B parameters. MIT license, clean commercial use. Tracked weakness: Short 16k context.

  6. #6DeepSeekOpen weights

    DeepSeek-V3

    DeepSeek's flagship 671B-parameter MoE, frontier-level quality at a tiny fraction of frontier prices.

    Context
    128K tokens
    Output · 1M
    $1.10 / 1M tokens
    Modalities
    text

    Why it ranks here. Frontier-level quality at open-weight prices. MIT license, clean commercial use. Tracked weakness: No native vision support.

  7. #7MistralOpen weights

    Mistral Nemo

    12B model co-built with Nvidia, strong small-model multilingual performance.

    Context
    128K tokens
    Output · 1M
    Pricing not published
    Modalities
    text

    Why it ranks here. Apache-2.0. Single-GPU fit. Tracked weakness: Quality limited by 12B size.

How to choose

Don't pick on the headline ranking alone. Run your top two picks on a representative sample of your own workload and compare. The numbers in this list are sound, but task-specific quality varies in ways no benchmark fully captures. The criteria above are the right axes to evaluate on, but the weighting depends on your stack.

  • Cost-sensitive workloads, start with the cheapest of the top three; escalate only if quality is the bottleneck.
  • Privacy-sensitive workloads, filter to open-weight picks above. They're labeled with a green badge.
  • Latency-sensitive workloads, see the Fastest LLMs list, which can override task-specific picks.

Frequently asked

  • What is the best model for local llms?
    Our #1 pick is Llama 4 8B from Meta. Meta's small Llama 4, built for on-device and edge inference.
  • How are these rankings determined?
    We rank by the criteria listed at the top of this page: Performance after 4-bit quantization; Memory footprint at int4 / int8; Inference speed on Apple Silicon and consumer GPUs. Where two models are close, we prefer the one with stronger production deployment evidence at the time of writing. Read the full methodology for our standards.
  • Llama 4 8B or Llama 4 70B?
    Both are top-tier picks. Llama 4 8B edges ahead on the criteria most relevant to this task. Llama 4 70B is the strongest alternative, see the head-to-head comparison page for full deltas.
  • Are open-source models on this list?
    Yes where they're competitive. Each entry below shows whether the model ships open weights and under what license.
  • How often is this list updated?
    Weekly. New launches that affect the ranking get reflected within seven days. The "last updated" stamp at the top of the page reflects the most recent dataset commit.

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