What is the best local AI model in 2026?

For most people, a 4-bit Qwen3 8B or Llama 3.1 8B on 8 GB of VRAM. On a 24 GB card, Qwen3 32B or Mistral Small 3.2 24B. There is no single best - pick by VRAM first, then by use case.

How much VRAM do I need for a local LLM?

Roughly 0.6-0.7 GB per billion parameters at 4-bit quantization plus ~1.5 GB overhead. An 8 GB card runs 8-9B models; a 24 GB card runs 27-32B models.

Is PewDiePie's Ajax a good local model?

Ajax is a Qwen 3.5 9B fine-tune built to be the agent brain inside Odysseus. It targets always-on private tasks rather than benchmark rankings, and it is refusal-ablated.

Which local models are truly open-license?

Apache-2.0 or MIT in this list: Qwen3 family, Mistral Small 3.2, Phi-4, DeepSeek-R1-Distill-Qwen-32B, and the Qwen coder models. Llama and Gemma use their own community licenses with restrictions.

Best local AI model for your GPU (October 2026)

Nine open-weight models that actually run on consumer hardware, compared by what matters: VRAM at 4-bit, license, and what each is good at. All rows verified against Hugging Face on 2026-10-07.

Quick answer

8 GB card: Qwen3 8B or Llama 3.1 8B at 4-bit.16 GB: Phi-4 14B or Mistral Small 3.2 24B (tight).24 GB: Qwen3 32B, Gemma 3 27B, or DeepSeek R1 Distill 32B.Two GPUs / 48 GB+: Llama 3.3 70B. Coding-first? See best local AI model for coding.

The table

Model	Params	License	Released	4-bit VRAM (est)	Best for
Qwen3 8B	8B	Apache-2.0	2025-04	~6.5 GB	Best small all-rounder; hybrid thinking modes
Qwen3 32B	32B	Apache-2.0	2025-04	~21 GB	Strong reasoning on a single 24 GB card
Llama 3.1 8B Instruct	8B	Llama 3.1 Community	2024-07	~6.5 GB	Ecosystem default, widest tooling support
Llama 3.3 70B Instruct	70B	Llama 3.3 Community	2024-11	~45 GB	Highest-quality chat short of a GPU server
Gemma 3 27B IT	27B	Gemma	2025-03	~18 GB	Multimodal (vision), strong multilingual
Gemma 3 4B IT	4B	Gemma	2025-02	~4 GB	Runs on almost anything incl. laptops
Mistral Small 3.2 24B	24B	Apache-2.0	2025-06	~16 GB	Long context, efficient single-card instruct
Phi-4	14B	MIT	2024-12	~10 GB	Small, strong at math/reasoning tasks
DeepSeek R1 Distill Qwen 32B	32B	MIT	2025-01	~21 GB	Reasoning (chain-of-thought) distilled

VRAM figures are estimates (0.6-0.7 GB per billion params at 4-bit + ~1.5 GB context overhead). "Released" is the HF repo creation date. Sources linked per row.

How to choose by VRAM

VRAM is the hard wall - a model that doesn't fit at a usable quantization is useless to you no matter how good it benchmarks. The 4-bit estimates above already include a small safety margin for context. If you're borderline, drop to a 3-bit quant of the same model rather than a smaller model at higher precision; quality tracks parameter count more than bit depth in this range.

Integrated GPU / laptop (4-8 GB shared): Gemma 3 4B is the only row that fits comfortably; Qwen3 8B works on 8 GB machines that can spare it.
8 GB card (3060, 4060, M-series 16 GB): 8-9B models at 4-bit. This is the tier Ajax itself targets - a Qwen 3.5 9B fine-tune sits right at this budget.
12 GB card: Phi-4 14B at 4-bit, or 8B models at 8-bit for better quality.
24 GB card (3090/4090): the sweet spot. 27-32B models at 4-bit, or everything smaller at higher precision.
48 GB+: 70B-class at 4-bit. Llama 3.3 70B is the reference point here.

License cheat-sheet

If you want zero strings attached, filter to Apache-2.0 and MIT: Qwen3 (all sizes), Mistral Small 3.2, Phi-4, and DeepSeek's R1 distills. Llama 3.1/3.3 and Gemma use custom community licenses that are fine for most personal and commercial use but carry named restrictions - read them if you're building a product. StarCoder2's OpenRAIL-M adds use restrictions; DeepSeek-Coder-V2-Lite uses DeepSeek's own license.

Where Ajax fits

PewDiePie's Ajax is a fine-tuned Qwen 3.5 9B built to be the always-on agent inside Odysseus - it has not been released yet (live status). On paper it lands in the same slot as Qwen3 8B: small enough for a single consumer GPU (est. ~6 GB at 4-bit per our requirements page), but tuned for agentic tool use rather than chat benchmarks, and refusal-ablated. If you need a general local model today, the 8-9B rows above cover the same hardware tier; if you want the always-on private-assistant experience Ajax is designed for, nothing else in the table is trained for Odysseus.

Caveats

This is a hardware-and-license comparison, not a benchmark leaderboard - scores move weekly and "best" depends on your workload. Every row links to the model card; check its eval table and community reports before committing disk space. If you're unsure, start with Qwen3 8B: Apache-2.0, 8 GB-friendly, and boring in the best way.