Running large language models locally has become increasingly accessible in 2026. From 8GB GPUs to massive multi-GPU rigs, enthusiasts and professionals alike are building powerful AI workstations right in their homes or offices.

This guide synthesizes community insights from r/localllama to provide a comprehensive overview of the current state of local LLMs.

Model Landscape in 2026

The Smartest Models You Can Run Locally

GPT-OSS-120B remains the gold standard for locally hosted models. Despite its massive size, it performs exceptionally well on consumer hardware when configured correctly:

  • Performance: Punches above its weight, scoring like a 200B parameter model
  • Speed: 200+ tokens/second on a 4090+64GB DDR4 rig
  • World Knowledge: Excellent for general knowledge tasks
  • Memory: Requires 64GB+ system RAM (or 16GB VRAM for smaller quantizations)

The key to running GPT-OSS-120B efficiently is offloading most of the model to system RAM while keeping a small portion in GPU VRAM. This approach allows it to run at usable speeds even on consumer hardware.

The Fastest Generalists

GLM-4.7-Flash and GLM-4.5 Air have emerged as excellent alternatives:

  • GLM-4.7-Flash: New 30B MoE model with competitive benchmarks

    • ~60% SWE Bench (comparable to Devstral Small 2)
    • MLA architecture for efficient KV cache usage
    • Supports 200k context window
    • Can run fully in GPU VRAM on modern hardware

  • GLM-4.5 Air: Smaller, faster option

    • Good all-rounder with derestricted capabilities
    • Excellent for general conversations and tasks

The Best for Coding

Devstral Small 2 (24B) and Qwen3 Coder 30B are top choices for programming tasks:

  • Devstral Small 2: 24B dense model with strong coding capabilities

    • 56.40% SWE Bench score
    • Excellent bash scripting assistance
    • Can work with agentic coding tools for complex tasks

  • Qwen3 Coder 30B: Faster than Devstral but slightly less performant

    • Great for quick coding tasks
    • Responsive and efficient

Specialized Models

Qwen3-TTS family offers impressive text-to-speech capabilities:

  • VoiceDesign: Create voices through textual descriptions
  • CustomVoice: Clone and fine-tune voices for specific speakers
  • Base model: 0.6B and 1.8B parameter versions
  • Languages: Supports 10 languages including English, Korean, Portuguese

Notable features:

  • Can run on 8GB VRAM GPUs with the 0.6B model
  • Voice cloning works well for single-speaker fine-tuning
  • Pipeline approach: Voice Design → Base Model → Voice Clone for reusability

Hardware Configurations

The 16GB VRAM + 64GB RAM Sweet Spot

This is one of the most common and versatile configurations in the community:

Recommended Models:

  1. GPT-OSS-120B (4-bit quant, mostly in RAM, some in GPU)
  2. GLM-4.5 Air or GLM-4.7 Flash (fully in VRAM)
  3. GPT-OSS-20B (for fast general tasks)
  4. Mistral Small 3.2 (24B) or Devstral Small 2 (24B)
  5. Gemma3 27B (if using small quantization)

Why This Works:

  • 24B dense models fit entirely in 16GB VRAM
  • 30B+ models can be partially offloaded to CPU RAM
  • You get a mix of speed and capability

Gaming GPU Optimizations

For gamers with 16GB GPUs, the key insight is to focus on dense 24B models rather than 30B+ sparse models:

  • Dense 24B models stay fast at long contexts
  • 30B+ models become slow at long contexts even with partial offloading
  • Qwen3 30B with expert CPU offloading can be faster than Gemma3 27B, but requires significant layer offloading (7-8 layers)

Multi-GPU Setups

High-end enthusiasts are building increasingly ambitious rigs:

  • 8x 3090: ~440GB VRAM total
  • 4x AMD R9700 (128GB VRAM): Massive parallel processing capacity
  • The ultimate mobile setup: 768GB RAM with 10x GPUs in an enclosed chassis

Practical Considerations:

  • Cooling becomes a major challenge with >3kW power draw
  • Airflow design is critical for sustained operation
  • Power distribution and safety are paramount

Best Practices

Model Selection Strategy

When choosing models, consider three factors:

  1. Hardware Constraints: What fits in your GPU and RAM?
  2. Primary Use Case: Coding, general conversation, creative writing, or specialized tasks?
  3. Speed vs. Quality: Do you need instant responses or can you wait a bit for better performance?

Example Selection:

  • For a 16GB GPU + 64GB RAM system: GPT-OSS-120B for knowledge, Devstral Small 2 for coding, GLM-4.5 Air for general tasks
  • For 8GB GPU: Qwen3-TTS 0.6B or smaller models for TTS, smaller generalist models for chat

Performance Optimization

For GPT-OSS-120B:

  • Use 4-bit quantization
  • Load model primarily in system RAM
  • Keep small portions in GPU for inference speed
  • Expect 12-30 tokens/second depending on setup

For 24B Models:

  • Run entirely in GPU VRAM for best performance
  • Use smaller context windows if needed
  • Enable optimizations specific to your inference framework

For MoE Models (like GLM-4.7-Flash):

  • MLAttention (MLA) architecture reduces KV cache memory usage
  • Allows for longer context windows (200k)
  • Can run at full speed on modern hardware

System RAM vs. GPU VRAM

A common question is how much of a model to load where:

  • System RAM: Cheap, large capacity, slower access
  • GPU VRAM: Fast, expensive, limited capacity

General Rule: Load the largest possible portion of the model in system RAM, keeping only what’s needed for inference in GPU VRAM. This provides the best balance of cost and performance.

RAG and Offline Resources

When building a truly offline system, knowledge sources are as important as the model:

Recommended Knowledge Bases:

  1. Wikipedia (kiwix format): 70-100GB, comprehensive and searchable
  2. Project Gutenberg: 236GB of public domain books
  3. Anna’s Archive: Includes sci-hub, libgen, and other sources (20TB+)
  4. Specialized repositories: Based on your specific needs

Pro Tip: A model grounded in 100GB+ of high-quality text will perform better than the “best” model without offline knowledge.

Community Insights

The AI Landscape

The r/localllama community has observed several important trends:

1. Convergence of Approaches Many developers are building similar tools (RAG systems, chatbots, agents). This is natural in the early stages of a technology - similar to how search engines looked in the late 1990s.

2. The Value of Local First Despite the hype, local models offer unique advantages:

  • Privacy and security
  • No API costs
  • No rate limits
  • True offline capability
  • Customization and fine-tuning

3. Hardware as a Differentiator As models become more accessible, hardware becomes the main differentiator. The community is seeing increasing specialization:

  • Gaming GPUs for general use
  • Professional GPUs for faster inference
  • Multi-GPU rigs for massive models
  • Custom cooling solutions for high-power setups

Common Pitfalls

1. Overestimating Model Performance Many users are impressed by benchmark scores but find models underwhelming in real-world use. Benchmarks don’t always reflect practical performance.

2. Neglecting Knowledge Bases A powerful model with no offline knowledge is like an encyclopedia without books. The most valuable systems combine models with comprehensive knowledge sources.

3. Ignoring Hardware Constraints Forcing a 120B model onto 16GB VRAM results in frustratingly slow performance. Understanding your hardware limitations is crucial.

Future Directions

The community is excited about several developments:

1. Better Compilation Support There’s growing demand for models that work well with compiled inference frameworks like llama.cpp rather than just Python/PyTorch.

2. Cross-Platform Support More support for AMD GPUs (ROCm), Apple Silicon, and Vulkan backends would democratize local AI.

3. Specialized Models Beyond general chat and coding, we’re seeing specialized models for:

  • Voice synthesis and voice cloning
  • Image generation
  • Audio processing
  • Scientific computing

Getting Started

For Beginners

  1. Start Small: Begin with an 8GB GPU and smaller models (7-8B parameters)
  2. Learn the Ecosystem: Understand different inference frameworks
  3. Build Incrementally: Start with a simple chat interface, add features as needed
  4. Focus on Use Cases: Build tools that solve your specific problems

For Power Users

  1. Optimize Hardware: Build a system that matches your use case
  2. Experiment with Models: Test different configurations and quantizations
  3. Build Custom Pipelines: Combine multiple models for specialized tasks
  4. Contribute to the Ecosystem: Share your findings and configurations

Essential Tools

  • Inference Frameworks: llama.cpp, vLLM, text-generation-webui
  • Model Repositories: Hugging Face, local model collections
  • Management Tools: LM Studio, Ollama, text-generation-webui
  • Knowledge Bases: Kiwix, specialized repositories

Conclusion

The local LLM ecosystem has matured significantly in 2026. What was once the domain of enthusiasts with massive budgets is now accessible to developers and hobbyists with a range of hardware configurations.

The key takeaways from the community are:

  • Hardware matters: Choose models that fit your hardware
  • Knowledge is power: Combine models with comprehensive offline resources
  • Start with a purpose: Build tools that solve real problems
  • Stay curious: The technology evolves rapidly

Whether you’re a gamer with a 16GB GPU or building a multi-GPU powerhouse, there’s never been a better time to run AI locally.

This guide synthesizes insights from the r/localllama community and reflects the state of local LLMs in early 2026. The landscape evolves quickly, so stay tuned for new developments.