The local LLM space moves fast. The model that was the obvious pick three months ago is probably not the obvious pick today. As of May 2026, here are the open-weight models worth running on your own hardware — by use case, by RAM, and by what you're actually trying to do.
Short version: Llama 4 70B is the best general-purpose local model if you have 64GB+. Qwen 3 32B is the best mid-tier pick. DeepSeek V3 is the best reasoning model. Gemma 3 27B is the best Apple Silicon option. None of them quite match Claude Sonnet 4.6 or GPT-5.4, but they're genuinely good now.
The hardware reality check
Before picking a model, know your ceiling. Real-world memory floor for usable inference at Q4 quantization:
- 8 GB — 7B models. Phi-4, Gemma 3 9B, Qwen 3 7B. Fine for chat, weak for code.
- 16 GB — 13B-class models. Llama 4 8B, Qwen 3 14B. The realistic mainstream sweet spot.
- 32 GB — 30B-class. Qwen 3 32B, Gemma 3 27B, DeepSeek-Coder V2 33B.
- 64 GB unified — 70B-class. Llama 4 70B, Qwen 3 72B at decent quantization. Frontier-adjacent quality.
- 128 GB+ — 100B+ models, Mixture-of-Experts variants like DeepSeek V3 (671B total / 37B active).
Apple Silicon's unified memory architecture remains the best price-per-GB story for local AI. A used Mac Studio M2 Ultra with 128 GB runs DeepSeek V3 well and costs less than a 4090 desktop build.
Llama 4 70B — Best general-purpose
Meta's Llama 4 family released in 2025 with major upgrades over Llama 3. The 70B variant is the best all-around local model in May 2026 — strong on reasoning, instruction following, English writing, and general code. License is permissive (Llama Community License) and allows commercial use for most companies under 700M users.
What it's not the best at: long-context reasoning past ~64K tokens (Claude and Gemini both extend further), and specialized coding (Qwen 3 Coder beats it on benchmarks). For everything else, Llama 4 70B is the default first download.
Qwen 3 32B — Best mid-tier and best at code
Alibaba's Qwen 3 family (released early 2026) is the surprise of the year. The 32B variant punches above its weight class on reasoning benchmarks and is genuinely best-in-class on coding tasks at the open-weight tier. The Qwen 3 Coder variant specifically targets code and outperforms Llama 4 70B on most coding benchmarks despite being half the size.
Qwen 3's other edge: bilingual quality. If you work in Chinese-language contexts (or just want models that handle non-English well), Qwen is the obvious pick. License is Apache 2.0 — permissive for commercial use.
DeepSeek V3 — Best for reasoning
DeepSeek V3's MoE architecture (671B total parameters, 37B activated per token) delivers reasoning performance that competes with GPT-4-class hosted models. The catch: you need ~256 GB to run it at usable quantization, which limits it to serious workstations or multi-GPU setups.
For smaller deployments, DeepSeek V2.5 (236B / 21B active) runs on 64-128 GB and remains the best reasoning model at that hardware level. License is MIT-style and allows commercial use.
Gemma 3 27B — Best for Apple Silicon
Google's Gemma 3 family (2025) is the best-optimized open-weight family for Apple Silicon's MLX runtime. The 27B variant runs faster than equivalent Llama or Qwen models on M-series chips because of MLX-targeted optimizations.
If your local AI lives on a MacBook Pro M4 or Mac Studio, Gemma 3 27B at MLX 4-bit gets you frontier-adjacent quality with the best speed. License is Gemma Terms of Use (permissive but with explicit prohibited-use clauses).
The smaller picks (8B-14B)
Don't sleep on smaller models. They're good enough for many tasks and run on consumer laptops:
- Llama 4 8B — The safest small-model pick. Great instruction-following.
- Qwen 3 14B — Best 16GB-RAM option. Excellent code performance for the size.
- Phi-4 (15B) — Microsoft's reasoning-focused small model. Punches above its weight on math/logic.
- Gemma 3 9B — Apple Silicon-optimized small option.
What about Mistral?
Mistral was the open-weight darling in 2023-2024. In 2026, the Mistral models (Mistral Large 2.5, Codestral) remain solid but no longer lead. Llama 4, Qwen 3, and DeepSeek have eclipsed them on most benchmarks. Worth knowing, not worth defaulting to.
The verdict
If you have 64GB+ unified memory: Llama 4 70B is the default. Add Qwen 3 Coder 32B for coding-specific tasks and DeepSeek V2.5 for reasoning-heavy work.
If you have 32GB: Qwen 3 32B is the best general-purpose pick. Gemma 3 27B if you're on Apple Silicon.
If you have 16GB: Qwen 3 14B for code, Llama 4 8B for general use, Phi-4 for math/reasoning.
For privacy-critical work: any of these on a properly air-gapped machine beats sending data to OpenAI or Anthropic. Trade quality for control.
For everything else: Claude Sonnet 4.6 or GPT-5.4 still win for production-quality output. Local LLMs are a powerful complement, not a replacement.
FAQ
What is the best local LLM in May 2026?
Llama 4 70B for 64GB+ users. Qwen 3 32B for tighter hardware. DeepSeek V3 for reasoning. Gemma 3 27B for Apple Silicon optimization.
What hardware do I need?
16GB for 13B models. 32GB for 30B-class. 64GB unified for 70B-class. For frontier-quality local AI, target 64GB unified memory on a Mac Studio or M4 Max MacBook Pro.
Llama 4 vs Qwen 3?
Llama 4 70B wins on general reasoning if you have the hardware. Qwen 3 32B is the better pick at smaller sizes and dramatically stronger on Chinese-language tasks and code.
Are local LLMs as good as Claude or GPT?
No, but the gap has shrunk. Llama 4 70B and Qwen 3 32B reach GPT-4 / Claude 3.5 level. Frontier hosted models still lead on long-context, agentic work, and code quality.
Best local LLM for coding?
Qwen 3 Coder 32B is the best dedicated coding local model. DeepSeek-Coder V2 is a close second. None match Claude Code or Codex for production agentic coding.