In this article you will learn about more than fifty open-source solutions for running large language models (LLMs) locally on your PC or server. You will get a full breakdown of categories (runtimes, web UIs, model families, edge/mobile formats), plus hardware guidance (system RAM, SSD storage, GPU/VRAM). If you are setting up a local AI assistant, chatbot, or experimentation platform, this guide will help you choose the right tool and hardware.

Why run LLMs locally (and key hardware questions)

Running a large language model locally brings benefits: full data privacy, offline access, cost savings compared to cloud API usage, and control over the model and code. But it also raises questions: how much VRAM do I need? Can I use CPU only? What storage requirements exist?
We’ll answer those here and then map each open­source option to its hardware guidance.

Key hardware variables:

• System RAM: your computer’s main memory.
• SSD (storage): where model files reside and possibly offload storage.
• GPU / VRAM: for inference on graphics card, the GPU memory matters when using fp16 or fp32, or with quantized int8/int4.
• CPU-only mode: possible for small quantized models but slow for larger ones.

Before diving into the list, here are rough hardware tiers:

• Small models (1B–6B parameters) → maybe 4–8 GB VRAM, ~16 GB system RAM, ~30 GB SSD.
• Mid models (7B–13B) → ~12–24 GB VRAM, 32–64 GB RAM, ~50–100 GB SSD.
• Large models (30B–70B+) → 48–100+ GB VRAM, 128+ GB RAM, maybe 200+ GB SSD.
  Quantization and optimized runtimes can reduce these by 2–8× in many cases.

Let’s now explore the categories and list the tools.

Category A: Lightweight CPU / embedded runtimes and libraries

These are best when you do not have a massive GPU or you want to run on laptop / embedded.
Open-source options include:

1. llama.cpp (ggml) – C/C++ inference for many LLaMA-style models

A minimalist runtime for running large language models in GGML / GGUF format on CPU or with minimal GPU. It enables models to run on lower VRAM or even purely CPU with quantization.
Hardware guidance: For a 7B model quantized to int4/int8 you might run on 8–16 GB system RAM plus ~20–30 GB SSD for model file. GPU is optional; VRAM requirement minimal if using CPU mode.

2. GGML / GGUF tooling (model format & quantization)

These formats and conversion tools enable optimized storage and inference of models in low-float or integer formats.
Hardware: Same as above; quantized files drastically reduce VRAM and RAM requirements.

3. GPT4All (Nomic) – packaged local models and easy interface

Designed for local use out of the box; supports CPU or GPU.
Hardware: Minimum ~8 GB system RAM; SSD space depends on model (≈10–30 GB). GPU optional for better speed.

4. llama.c (Windows or ports) – ultra light ports

Ports for lower-power machines or laptops.
Hardware: Suitable for small quantized models on laptops with 8–16 GB RAM. GPU may be integrated iGPU.

5. GGML bindings (Python / JavaScript) – runtime bindings

These libraries help run GGML models in languages like Python or JS.
Hardware: Follows ggml requirements; very useful for embedding models in applications without heavy hardware.

Category B: Local servers / web UIs and orchestration

These provide user-friendly interfaces or local servers to make it easier to run models and interact with them.

6. text-generation-webui (oobabooga)

A popular web UI frontend supporting many backends (PyTorch, bitsandbytes, ggml).
Hardware: For 7B models you may need ~8–14 GB GPU VRAM; for 13B models you’ll get comfort with ~24 GB VRAM. System RAM around 32 GB recommended.

7. LocalAI – fast local server with automatic backend detection

Fits use-cases where you want a REST API locally.
Hardware: GPU recommended for interactive speed; 12-24 GB VRAM sweet spot for mid sized models.

8. Ollama – model management + runner

Simplifies installation and switching models; supports GPU acceleration.
Hardware: Discrete GPU preferred; integrated GPU may limit model size or speed.

9. LM Studio – desktop GUI for local LLMs

Supports Vulkan offload for laptops/mini-PCs.
Hardware: Works even on machines with integrated GPU; for decent performance a discrete 12GB+ GPU is recommended.

10. KoboldAI – storytelling UI + multiple backends

Ideal if you want creative applications.
Hardware: Varies by backend; for modern models plan for ~12 GB VRAM or more.

11. LocalAGI – agentic stack for local models

Focuses on building agents locally rather than just chat.
Hardware: Depending on complexity; GPU recommended for mid/large models.

Category C: Low-level acceleration & quantization tools

These tools help you get more out of your hardware by reducing memory, boosting speed, or enabling larger models on smaller systems.

12. bitsandbytes – 8-bit optimizers & loaders

Allows you to load models in 8-bit (int8) to reduce GPU memory usage by about half compared to fp16.
Hardware: With int8 you can run 13B models on 12-24 GB VRAM cards depending on other factors.

13. AutoGPTQ – automatic quantization for faster inference and lower VRAM

Focuses on converting models to int4/int8 with minimal loss of quality.
Hardware: After quantization, models that previously needed 24+ GB VRAM might run on 12-16 GB VRAM at reduced performance cost.

14. ExLlama – fast CUDA inference kernels for LLaMA derivatives

Optimized for NVIDIA GPUs; improves throughput and efficiency for large models.
Hardware: Best with Nvidia cards; helps when using 24-48 GB VRAM for 30B models.

15. GGUF/GGML quantizers (TheBloke tools)

Community tools to convert many popular model checkpoints to optimized GGUF format for minimal hardware.
Hardware: Makes many large models viable on 12-24 GB VRAM systems when quantized properly.

Category D: Hugging Face / PyTorch / Transformers ecosystem

The “standard” deep-learning framework approach to local inference or training.

16. Hugging Face Transformers

Large ecosystem supporting many models, fine-tuning, inference.
Hardware: Running models in fp16 or fp32 often needs higher VRAM than optimized runtimes. For example a 13B model may need 24–30+ GB VRAM.

17. Text Generation Inference (TGI) – Hugging Face

Designed for high-performance inference servers for large models.
Hardware: Multi-GPU or high-VRAM setups are typical; good for production rather than hobby.

18. Accelerate – Hugging Face

Allows multi-GPU, offload, distributed inference.
Hardware: If you have two or more GPUs or GPU+CPU offload, you can run 30B+ models locally.

Category E: Major open-source model families (to run locally)

These are individual model checkpoint families you can download and run locally. I’ll group by size class to make hardware guidance easier.

Small / portable (1B–6B)

19. GPT-J (6B) – easier to run; ~12 GB VRAM in fp16; smaller with quantization.
20. Pythia small sizes – 1–6B models suitable for desktops.
21. Alpaca / Alpaca-style fine-tuned models (7B) – hardware similar to 7B class below.

Mid class (7B–13B)

22. Mistral-7B – efficient 7B model; ~12–16 GB VRAM in fp16.
23. Llama-2 / Llama-3 (7B) – runs well on 12-16 GB VRAM with quantization.
24. Vicuna-7B / Vicuna-13B – instruction-tuned; expect ~16–24 GB VRAM for 13B in fp16.
25. MPT-7B / MPT-13B – runs comfortably on 16-24 GB VRAM.
26. Baichuan-7B – similar profile.
27. Mixtral-7B / 13B – high-performance; 13B version likely ~24+ GB VRAM.
28. CodeLlama-7B – code-specialized; VRAM similar to text-models of same size.

Large class (30B–70B+)

29. Falcon-40B – requires ~48–100+ GB VRAM in fp16; but quantization and sharding let you run on smaller setups (with trade-offs).
30. Llama-2-70B – very heavy; multi-GPU or offload required.
31. GPT-NeoX-20B+ – 20B model requires ~40+ GB VRAM.
32. RedPajama-30B – similar scale.
33. BLOOM-176B – server-class.
34. OPT-175B – very large; not practical for most local setups.
35. Gemma family large sizes – scale accordingly.
36. TheBloke GGUF quantized versions of large models – make them feasible on smaller hardware via quantization.
37. RWKV large versions – memory efficient but still large.
38. Dolphin (Databricks Dolly large) / Orca large versions – heavy hardware needed.
39. Koala large sizes – similar.
40. Qwen / Other Chinese large language model families – hardware scale matches size.

Category F: Edge / Mobile formats and toolchains

These allow inference on laptops, small PCs, or mobile/embedded devices.

41. CoreML / Apple Metal runtimes – for Macs with M1/M2. A 7B model may run with 16–32 GB unified memory.
42. ONNX Runtime with quantization – convert models to ONNX and run on CPU or other accelerators.
43. Apache TVM – compile models for embedded inference.
44. (bonus) WebAssembly / browser-based quant models – some very small models run fully in browser.

Category G: Full-stack servers, containers, and orchestration

For enterprise or heavy local deployment.

45. Docker images for Text Generation Inference – containerised production setups.
46. KServe / KFServing – Kubernetes-based serving for LLMs.
47. Ray + Serve – distributed inference orchestration.
48. NVIDIA Triton Inference Server – multi-GPU high-performance serving.
49. Multi-node GPU clusters – running large models across nodes.

Category H: Research and experimental runtimes / novelty

50. RWKV.cpp – CPU-friendly implementation of RWKV architecture; runs smaller versions on modest hardware.
51. FlashAttention variants / fused kernels – for high throughput but still hardware demanding.
52. Community forks of ggml/llama.cpp targeting extreme quantization and small GPUs.
53. Experimental browser-based local LLM runtimes (edge WebGPU) – still early but promising.

How to choose your hardware for local LLMs

You’ll need to decide: what model size will you use, what budget/PC do you have, how fast must inference be? Use this process:

1. Decide model size (7B vs 13B vs 30B).
2. Check VRAM budget: Do you have 12 GB card, 24 GB, or more?
3. Pick runtime: If you use optimized runtime/quantization you may cut VRAM—so a 12 GB GPU may support a 13B model if quantized.
4. Check system RAM and SSD: For a 13B model, plan 32–64 GB RAM and 50–100 GB SSD.
5. Consider future growth: If you may want to run 30B later, buy a 24 GB or 40+ GB GPU or a dual-GPU rig.

Here are budget tiers:

• Entry hobbyist build: 32 GB system RAM, 1 TB NVMe SSD, GPU ~12 GB VRAM. Good for 7B models or small 13B quantized.
• Power user build: 64 GB RAM, 2 TB SSD, GPU ~24 GB VRAM (e.g., 3090/4080). Can run many 13B models comfortably and some quantised 30B.
• Server/enthusiast build: 128 GB+ RAM, multi-TB NVMe, GPU(s) 40+ GB VRAM each or dual/tri GPU. For 30B–70B class.

Hardware requirement details by model-size category

Here is an expanded table with more details and explanation for each model size category. Use it as a planning reference.

Note: Quantization (int8, int4) can reduce the VRAM needs significantly—sometimes by half or more. If you use a runtime that supports quantization and efficient memory offload, you can stretch smaller hardware further.

Best workflow for getting started with local LLMs

1. Begin with a 7B model and a friendly UI (e.g., text-generation-webui or LM Studio).
2. Use a GPU of at least 12 GB VRAM, 32 GB system RAM, 1 TB SSD to store several models.
3. Choose a runtime that supports quantization (bitsandbytes / AutoGPTQ) to lower VRAM usage.
4. When you’re comfortable, move to 13B models and test performance.
5. If you have access to 24+ GB VRAM or multi-GPU, experiment with 30B+ models.
6. Use model families and format conversion tools to ensure compatibility with your hardware.
7. Monitor inference latency, memory usage, and model behaviour. Consider offload or model-sharding for large models.

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Frequently Asked Questions (FAQ)

Q: Can I run a 70 B-parameter model on a normal desktop GPU?
A: Not realistically in fp16/float32 without heavy hardware. You would need 100+ GB VRAM or use advanced quantization and sharding frameworks. Better to start with 7B or 13B models.

Q: Is GPU absolutely required for local LLM inference?
A: No, but GPU greatly speeds up inference. You can run very small models on CPU only (especially with llama.cpp/ggml), but latency will be higher.

Q: What is the best model size for a 12 GB VRAM GPU?
A: A 7B model is the most practical. With quantization, some 13B models can run but expect trade-offs in speed or accuracy.

Q: Does storage (SSD) matter a lot?
A: Yes. Many model files are tens of gigabytes, and you’ll likely want fast NVMe SSD for quick load and offload support. A 1 TB SSD is a good safe starting point.

Q: How do I future-proof my hardware if I might later want 30B models?
A: Invest in at least a 24 GB VRAM GPU, 64 GB_SYSTEM_RAM, and NVMe SSD 2 TB+. That gives you room to grow to 30B class models with quantization or offloading.

Conclusion

This article covered over fifty open-source tools and model families for running large language models locally. We grouped them by runtime type, UI/serving layer, model family, edge formats, orchestration stacks, and experimental options. We provided hardware guidance (RAM, SSD, GPU/VRAM) and a competitor-style analysis of different approaches.
If you are ready to experiment with local LLMs, start with a 7B model on modest hardware, pick a user-friendly UI, and consider quantization to make better use of your GPU. Then scale up to 13B or 30B as your budget and hardware allow.