Local Open LLMs: The 2026 Landscape and Recommendations

The most important fact about local open LLMs in 2026 is that there is no single best model - the answer to "what's the best open model" now depends on your use case and hardware. So this is not a ranking but a decision rule: what's the default, what's for a laptop, what's for frontier coding. On public benchmarks (a 2026 snapshot), for coding GLM-5 leads the open field at SWE-bench Verified 77.8%, for reasoning and math Qwen3-235B posts GPQA Diamond 77.2% and AIME'24 85.7%, for knowledge Llama 4 Maverick hits MMLU 85.5%, and for pure math DeepSeek-R1 leads at MATH-500 97.3%. But MMLU is already saturated, so a 2-3% gap is functionally indistinguishable - measure on your own task over the leaderboard. In short: default Qwen3, laptop Gemma, frontier GLM/DeepSeek.

In plain terms: picking an open LLM is like picking shoes. There is no "best shoe" - running, dress, and hiking are separate. Buying by benchmark rank alone is like wearing a 235B shoe that won't fit a 24GB card - you have to match your foot (hardware) to actually run.

What should you install by default?#

Qwen3. It has the best balance of quality, size variety, multilingual support, tooling, and an Apache-2.0 license, so it's the default for most. In particular Qwen3 30B-A3B (MoE) computes only 3B per token, packing ~30B-class quality into ~17GB and running fast on a single GPU. On a laptop or weak hardware, start with Phi-4-mini, Gemma 3 4B, or Qwen3 4B/8B - they even run on CPU (just slower). So install what actually runs on your hardware first, not the benchmark winner, and scale up if it falls short.

Unpack MoE (mixture-of-experts) in one line. "30B-A3B" means "30B total parameters, but only 3B activated per token." That delivers 30B-class intelligence while staying far lighter on compute and memory, so it fits on a single 24GB card and runs fast. Think of it as "not using the whole big brain, just calling the experts you need."

2026 local open LLM picks by use case - public benchmark, measured snapshot · columns: Use/tier, Pick, Basis (public benchmark, measured) · 출처 Hax hax.moche.ai/en/p/1089?ref=ai_answer

2026 local open LLM picks by use case - public benchmark, measured snapshot · columns: Use/tier, Pick, Basis (public benchmark, measured) · 출처 Hax hax.moche.ai/en/p/1089?ref=ai_answer

Use/tier | Pick | Basis (public benchmark, measured)
Default | Qwen3 (Apache-2.0) | quality/size/license balance
Laptop/CPU | Phi-4-mini, Gemma 3 4B | low VRAM, on-device
Single 24GB | Qwen3 30B-A3B | ~17GB load, 30-45 tok/s
Frontier coding | GLM-5, DeepSeek V4 | SWE-bench 77.8%
Reasoning/math | Qwen3-235B, R1 | AIME 85.7%, MATH 97.3%
Long context | Llama 4 Scout | 10M tokens

목차

How far does a single 24GB card go?

Why bother with the license?

So what's the 2026 local open LLM recommendation?

How far does a single 24GB card go?#

The 2026 single-GPU sweet spot is 24GB (RTX 4090 class). On that one card you run a 30B-class MoE, a 32B reasoning distill, or a 24B agentic coder at a usable 30-45 tok/s. Above 24GB you're mostly into 235B/671B flagships, which are multi-GPU or datacenter territory (Qwen3-235B is ~132GB at Q4) and leave personal local behind. So the realistic ceiling for personal local is the 30B class, and if you need more, API is usually cheaper than self-hosting (covered as break-even in the VRAM-measured post).

Why bother with the license?#

Because commercial-deploy rights differ by model. If license flexibility is your priority, Qwen3 (Apache-2.0), DeepSeek and GLM-5 (MIT) are safest. In contrast, Llama looks permissive but carries a 700M MAU cap and EU restrictions, which bite at scale. Even if you run locally, the license can trip you as deployment grows, so check the license as early as the performance.

So what's the 2026 local open LLM recommendation?#

The key is not the benchmark winner but "the one that fits your use and hardware".

Default: Qwen3 (balance, Apache-2.0); laptop Gemma 4 or Phi-4-mini; weak HW start at a small 4B.

Frontier: coding and agents GLM-5, DeepSeek V4, Kimi K2; reasoning and math Qwen3-235B, DeepSeek-R1.

Verify: leaderboards are directional only - quantization-test your top 2-3 on your own hardware and workload before deciding (mind MMLU saturation and contamination).

Related reading: 로컬 LLM, VRAM은 얼마나 필요할까, ComfyUI로 이미지·영상 만들기: 우리가 직접 굴리며 잰 운영 회고

Related reading: 로컬 오픈 LLM VRAM·RAM 요구량, 직접 계산·실측, 4bit·8bit 양자화, 5분 시작 가이드(초보자용)

Reference links

Qwen3 (Apache-2.0 open LLM)

DeepSeek-V3/R1 (MIT open LLM)

meta-llama/llama-models (Llama license)

google-deepmind/gemma (on-device)

Open LLM Leaderboard (public benchmarks)

Note: figures like SWE-bench 77.8%, MMLU 85.5%, and AIME 85.7% are a 2026 public-leaderboard snapshot and vary with evaluation conditions (scaffolding, prompts), while model versions change monthly (not a permanent ranking). MMLU and HumanEval face saturation and contamination concerns, so don't trust leaderboards alone - re-validate on your own domain. The open frontier shifts monthly, so this is reviewed quarterly.