Best Local LLMs (March 2026)

Running LLMs locally gives you privacy, zero API costs, and offline capability. Here are the best models to run on your hardware right now.

Quick Recommendations

Your VRAM	Best Model	Use Case
8GB	Qwen 3.5 9B (Q4_K_M)	All-rounder
16GB	GLM-4.7-Flash 8.0	General + coding
24GB	LFM2:24B	Complex reasoning
32GB+	DeepSeek V3 (when available)	Production workloads

Top Models (March 2026)

Tier 1: Best Performance

Model	Size	VRAM (Q4_K_M)	Strengths
Qwen 3.5 9B	9B	6.6GB	Beats models 3x its size
GLM-4.7-Flash	8B	5.5GB	Fast, excellent Chinese/English
LFM2:24B	24B	16GB	Best reasoning at size

Tier 2: Excellent Options

Model	Size	VRAM (Q4_K_M)	Strengths
Llama 3.3 70B	70B	45GB	Meta’s best open model
Mistral Large	32B	22GB	Strong European model
DeepSeek R1	7B	4.8GB	Best thinking/reasoning
Phi-4	14B	10GB	Microsoft’s latest

Tier 3: Budget/Edge

Model	Size	VRAM	Strengths
Qwen 2.5 3B	3B	2.5GB	Tiny but capable
Gemma 3 4B	4B	3GB	Google’s efficient model
TinyLlama	1.1B	1GB	Microcontroller-capable

Community Top Picks (r/LocalLLaMA)

From the March 2026 “What’s the best local LLM?” thread:

“GLM-4.7-Flash 8.0 via Ollama, which is amazing. And currently downloading LFM2:24B.”

“Qwen 3.5 9B has since taken this spot—it fits in 6.6GB via Ollama and beats models 3x its size on reasoning benchmarks.”

VRAM Requirements Guide

By Model Size (Q4_K_M Quantization)

Model Size	VRAM Needed	Example Models
3B	2-3GB	Qwen 2.5 3B, Phi-3 Mini
7B	4-5GB	DeepSeek R1, Mistral 7B
9B	5-7GB	Qwen 3.5 9B
13B	8-10GB	Llama 3.2 13B
24B	14-18GB	LFM2:24B
34B	20-24GB	CodeLlama 34B
70B	40-48GB	Llama 3.3 70B

By GPU

GPU	VRAM	Recommended Models
RTX 3060	12GB	Up to 13B
RTX 3080	10GB	Up to 13B
RTX 4070	12GB	Up to 13B
RTX 4080	16GB	Up to 24B
RTX 4090	24GB	Up to 34B
M1/M2/M3 Max	32-64GB	Up to 70B
M4 Pro	24GB	Up to 34B

Setting Up: Ollama vs LM Studio

Ollama (Recommended for Most)

Best for: CLI users, API integration, production use

Install:

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

# Run a model
ollama run qwen3.5:9b

Configure for OpenAI-compatible API:

# Starts server at localhost:11434
ollama serve

LM Studio

Best for: GUI users, exploring models, non-technical users

1. Download from lmstudio.ai
2. Browse and download models
3. Start local server for API access

Using Both (Power User Setup)

The recommended 2026 stack:

• Ollama as the inference engine (production)
• LM Studio for exploring and testing new models
• OpenWebUI for ChatGPT-like interface

Model Performance Comparison

Coding Benchmarks (March 2026)

Model	HumanEval	MBPP	Real Coding
Qwen 3.5 9B	72.1%	68.4%	⭐⭐⭐⭐
GLM-4.7-Flash	69.8%	65.2%	⭐⭐⭐⭐
DeepSeek R1 7B	71.5%	67.8%	⭐⭐⭐⭐
LFM2:24B	78.3%	74.1%	⭐⭐⭐⭐⭐
GPT-4 (reference)	85.4%	80.2%	⭐⭐⭐⭐⭐

General Reasoning

Model	MMLU	Common Sense
Qwen 3.5 9B	78.2%	Strong
GLM-4.7-Flash	76.8%	Very Strong
LFM2:24B	84.1%	Excellent

Best for Specific Use Cases

Coding

1. LFM2:24B (if you have VRAM)
2. Qwen 3.5 9B (best efficiency)
3. DeepSeek R1 7B (reasoning-focused)

Chat/Conversation

1. GLM-4.7-Flash (natural dialogue)
2. Qwen 3.5 9B (versatile)
3. Mistral 7B (snappy responses)

Writing

1. Qwen 3.5 9B (creative)
2. GLM-4.7-Flash (multilingual)
3. Phi-4 14B (instruction-following)

Research/Analysis

1. LFM2:24B (deep reasoning)
2. DeepSeek R1 (thinking process)
3. Qwen 3.5 9B (efficient)

Quantization Guide

Which Quantization to Use

Quantization	Quality	Size Reduction	When to Use
FP16	Best	None	Have lots of VRAM
Q8_0	Excellent	~50%	Plenty of VRAM
Q6_K	Very Good	~60%	Good balance
Q4_K_M	Good	~70%	Most common choice
Q4_K_S	Decent	~75%	Tight on VRAM
Q3_K_M	Acceptable	~80%	Very limited VRAM

Command Examples

# Ollama defaults to Q4_K_M
ollama run qwen3.5:9b

# Specify quantization
ollama run qwen3.5:9b-q6_k

Integration with Tools

With Claude Code

# Set Ollama as backend
export ANTHROPIC_API_BASE=http://localhost:11434/v1
claude-code --model qwen3.5:9b

With Cursor

1. Settings → Models → Add Custom
2. Enter Ollama endpoint
3. Select model

With OpenWebUI

docker run -d -p 3000:8080 \
  -v open-webui:/app/backend/data \
  --add-host=host.docker.internal:host-gateway \
  ghcr.io/open-webui/open-webui:main

Mac-Specific Notes (M1/M2/M3/M4)

Unified Memory Advantage

Apple Silicon’s unified memory means you can run larger models:

Chip	RAM	Max Model Size
M1/M2 (16GB)	16GB	~13B at Q4
M1/M2 Max (32GB)	32GB	~30B at Q4
M2/M3 Max (64GB)	64GB	~70B at Q4
M4 Pro (48GB)	48GB	~50B at Q4

Best Mac Local Setup

# Install Ollama
brew install ollama

# Run with GPU (automatic on Mac)
ollama run qwen3.5:9b

What’s Coming

DeepSeek V4 (Expected March 2026)

• 1M token context
• Open-source weights
• Multimodal capabilities
• Will likely be the new king of local LLMs

Llama 4 (Expected 2026)

• Meta’s next generation
• Likely 8B-405B range
• Open weights

Bottom Line

For most users in March 2026:

1. Install Ollama
2. Run ollama run qwen3.5:9b
3. Get 90% of GPT-4 quality for free

The gap between local and cloud models continues to close. With DeepSeek V4 coming soon, running frontier-level AI locally is becoming realistic.

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Last verified: March 12, 2026