Best for Local (2026)
Best local LLM in 2026
2026 is the first year where running a genuinely useful LLM on consumer hardware is table stakes. A modern 8B or 14B model at Q4_K_M outperforms GPT-3.5 across most benchmarks, and a 32B fits comfortably on a single RTX 4090 or a 32 GB Apple Silicon Mac.
We optimise for quality-per-VRAM at Q4 on 24 GB, fast tokens-per-second on consumer GPUs, and broad ecosystem support across Ollama, llama.cpp, LM Studio and MLX.
Running locally means no per-token bills, no rate limits, no data leaving the machine. In 2026 the quality gap with hosted frontier models is smaller than it's ever been.
Our picks
The default 2026 local pick. Apache 2.0, reasoning-mode toggle, strong at ~6 GB VRAM at Q4.
Meta's workhorse 8B. Best ecosystem support of any local model.
Fits a 4090 at Q4 with real context. Reasoning-mode enabled.
14B synthetic-data-trained; punches at 32B-class quality on reasoning.
70B on Apple Silicon 64 GB or 4090 at aggressive Q4. The realistic ceiling for local.
Pocket-sized fallback for laptops and small VRAM budgets.
Things to watch out for
- Quantization matters. Q4_K_M is the mainstream default; drop to Q5_K_M if you have the VRAM headroom.
- Effective context is often much smaller than advertised. Test with realistic long-context prompts before committing.
- Ollama is the easiest starting point. LM Studio if you want a GUI. vLLM for production hosting on your own hardware.
- Apple Silicon: MLX ≥ llama.cpp Metal ≥ Ollama in raw speed. Unified memory is more flexible than dedicated VRAM.
All picks at a glance
The April 2025 refresh of Qwen at 8B. Native mixed-mode reasoning: the model can 'think' before answering when triggered, or answer directly for simple queries — configurable per request. Apache 2.0. A strong upgrade over Qwen 2.5 7B on math and code, with much better instruction following.
- Context
- 33K
- License
- apache-2-0
- VRAM Q4
- 4.8 GB
The workhorse 8B instruction-tuned model. Excellent quality-to-cost ratio and the broadest ecosystem support of any open-weights model — every major inference engine, fine-tuning library, and quantization toolchain has a 3.1 8B preset. Fits in 24 GB of VRAM at fp16, ~6 GB at Q4. Strong default for production chat where 70B is overkill, for fine-tuning on a specialist task, and for any workload where you want a known-good baseline.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 4.8 GB
32B sweet-spot Qwen 3, Apache 2.0. Reasoning-mode toggle inherited from smaller siblings; strong on math, code and agentic tool use. Fits on a single H100 in fp16 and on a 4090 at Q4.
- Context
- 33K
- License
- apache-2-0
- VRAM Q4
- 19.2 GB
14B model trained primarily on synthetic data. Punches above its weight on reasoning, especially MATH and GPQA. MIT licensed. A standout choice when you want strong reasoning quality without paying 70B-tier hardware costs. Phi-4 in particular demonstrated that careful synthetic-data curation can extract frontier-class reasoning from a relatively small dense model.
- Context
- 16K
- License
- mit
- VRAM Q4
- 8.4 GB
Meta's December 2024 refresh of Llama 3 70B that closes most of the gap with Llama 3.1 405B for chat workloads while remaining tractable on a single H100. Strong instruction following, robust tool-use behaviour, and a 128K context window make it the default choice for production chat at 70B scale. The 3.3 release was trained on a refreshed instruction-tuning data mix and benefits from Meta's most recent alignment work. It outperforms the much larger 3.1 405B on several reasoning benchmarks at a fraction of inference cost. The licence is the Llama 3 Community License, which permits commercial use unless your service exceeds 700M monthly active users. Good pick for: production chat at scale, RAG over long documents, agentic workflows where tool use matters, and any 70B-tier replacement for closed proprietary models.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 42 GB
Pocket-sized Llama 3 variant for edge deployment. Surprising chat quality after instruction tuning makes it competitive with much larger models from a previous generation. At Q4 it fits in ~2 GB of VRAM and runs on consumer GPUs and recent Apple Silicon. A strong default for on-device chat, summarisation, and structured extraction tasks where the workload doesn't need frontier reasoning quality.
- Context
- 128K
- License
- llama-3
- VRAM Q4
- 1.8 GB