The 8B parameter class is the sweet spot for local AI. These models run on any modern laptop with 8 GB RAM, respond in seconds, and are surprisingly capable. Here are the best ones in 2026.

The ranking

Rank	Model	Active params	RAM (Q4)	Overall quality
🥇	Gemma 4 E4B	4.5B	4 GB	⭐⭐⭐⭐
🥈	Qwen 3.5 Flash	~8B	5 GB	⭐⭐⭐⭐
🥉	Llama 4 Scout 8B	8B	5 GB	⭐⭐⭐
4	Gemma 4 26B MoE	3.8B active	8 GB	⭐⭐⭐⭐⭐
5	Phi-3.5 Mini	3.8B	3 GB	⭐⭐⭐
6	MiMo V2 Flash	~15B active	10 GB	⭐⭐⭐⭐

Wait — Gemma 4 26B at #4? Yes. Despite having 26B total parameters, it only activates 3.8B per inference. In terms of compute cost and speed, it behaves like a small model while delivering medium-model quality. It’s the cheat code of this category.

#1: Gemma 4 E4B

Google’s edge model supports text, images, AND audio — the only sub-8B model with triple modality. At 4 GB RAM (Q4), it runs on virtually anything.

ollama run gemma4:e4b

Strengths: Multimodal, tiny footprint, 128K context, Apache 2.0. Weakness: Not as strong on complex reasoning as larger models. Best for: Mobile apps, voice assistants, edge devices.

See the full Gemma 4 family guide for specs and benchmarks.

#2: Qwen 3.5 Flash

Alibaba’s smallest Qwen 3.5 model. Excellent at coding and multilingual tasks for its size.

ollama run qwen3.5:flash

Strengths: Best coding ability in this size class, strong multilingual support, Apache 2.0. Weakness: Text-only, no multimodal. Best for: Code completion, multilingual chatbots, quick text tasks.

#3: Llama 4 Scout 8B

Meta’s entry in the small model space. Part of the Llama 4 family.

ollama run llama4:scout-8b

Strengths: Good general knowledge, large community, extensive fine-tunes available. Weakness: Llama license (not fully open), weaker on coding than Qwen. Best for: General-purpose chatbot, RAG applications.

#4: Gemma 4 26B MoE (the cheat code)

This is technically a 26B model, but its MoE architecture means only 3.8B parameters activate per inference. It runs at small-model speeds while delivering medium-model quality.

ollama run gemma4:26b

Strengths: Best quality in this compute class by far. 256K context. Multimodal. Weakness: 8 GB RAM at Q4 — tight for some laptops. Larger download. Best for: Anyone with 8+ GB RAM who wants the best possible local AI. See our setup guide.

#5: Phi-3.5 Mini

Microsoft’s compact model. At 3.8B parameters, it’s the smallest model here that still produces coherent, useful output.

ollama run phi3.5:mini

Strengths: Tiny (3 GB RAM at Q4), fast, good at structured tasks. Weakness: Struggles with creative writing and complex reasoning. Best for: Constrained hardware, Raspberry Pi, embedded systems.

#6: MiMo V2 Flash

Xiaomi’s open-source model uses MoE with ~15B active parameters. It’s at the upper end of this category but delivers excellent results.

ollama run mimo-v2-flash

Strengths: Strong coding, fast inference, open source. Weakness: 10 GB RAM at Q4 — needs a decent machine. Best for: Coding tasks where you need more power than 8B models offer. See our local setup guide.

Hardware requirements

Model	RAM (Q4)	RAM (Q2)	CPU speed	GPU speed
Phi-3.5 Mini	3 GB	2 GB	15 tok/s	40 tok/s
Gemma 4 E4B	4 GB	3 GB	12 tok/s	35 tok/s
Qwen 3.5 Flash	5 GB	3 GB	10 tok/s	30 tok/s
Llama 4 Scout 8B	5 GB	3 GB	10 tok/s	30 tok/s
Gemma 4 26B MoE	8 GB	5 GB	8 tok/s	25 tok/s
MiMo V2 Flash	10 GB	6 GB	6 tok/s	20 tok/s

All speeds are approximate on a modern laptop CPU (M2/Ryzen 7) or a mid-range GPU (RTX 3060/4060).

For the absolute minimum hardware, see best AI models under 4GB RAM. For GPU recommendations, check our GPU buying guide.

How to run any of these

The fastest path is Ollama:

# Install
curl -fsSL https://ollama.com/install.sh | sh

# Run any model
ollama run gemma4:e4b
ollama run qwen3.5:flash
ollama run llama4:scout-8b

For more control over quantization and settings, use llama.cpp. For production serving, use vLLM. See our runtime comparison for details.

Which one should you pick?

Absolute minimum hardware (2-4 GB RAM): Phi-3.5 Mini or Gemma 4 E2B

Standard laptop (8 GB RAM): Gemma 4 26B MoE — it’s the best quality you can get at this hardware level

Coding focus: Qwen 3.5 Flash or Qwen 2.5 Coder 7B

Multimodal (images + audio): Gemma 4 E4B — nothing else in this size class does it

Maximum quality (10+ GB RAM): MiMo V2 Flash

For a broader comparison including larger models, see our best local AI models by task ranking and cheapest way to run AI locally.

The bottom line

Small models in 2026 are genuinely useful. Gemma 4’s MoE trick — 26B total but 3.8B active — means you get medium-model quality at small-model cost. If you have 8 GB of RAM, there’s no reason not to run AI locally. It’s free, it’s private, and it’s fast enough for real work.

FAQ

What’s the best 8B parameter model in 2026?

Gemma 4 12B (MoE with 3.8B active parameters) offers the best quality at 8B-class resource usage. For pure 8B models, Qwen 3.5 9B and Llama 4 Scout 8B are the strongest options, with Qwen slightly ahead on coding tasks.

Can 8B models do real coding work?

Yes, for routine tasks. 8B models handle autocomplete, simple refactors, boilerplate generation, and code explanation well. They struggle with complex multi-file reasoning and architectural decisions. Use them for speed and privacy, escalating to larger models for hard problems.

How much RAM do I need for an 8B model?

At Q4 quantization, 8B models need about 5GB of RAM/VRAM. They run comfortably on any machine with 8GB+ total RAM, including laptops without dedicated GPUs (though inference will be slower on CPU).

Related: AI Coding Tools Pricing