Top Local LLMs 2025: Context Windows, VRAM Targets and License Guide

Why this list matters

Local large language models matured rapidly in 2025. Open-weight families and well documented model cards now make on-prem and even laptop inference practical if you match context length and quantization to available VRAM. This guide compares ten deployable local LLM options by license clarity, stable GGUF availability, and predictable performance characteristics including parameters, context length, and quant presets.

How to pick a local LLM

Choose by three axes: context window, license and ecosystem, and hardware budget. Dense models tend to offer predictable latency and simpler quantization. Sparse Mixture of Experts models can deliver higher throughput per cost when you have the VRAM and parallelism to exploit them. Small, long-context models are the sweet spot for CPU and integrated GPU devices.

Top 10 deployable local LLMs (2025)

1) Meta Llama 3.1-8B — robust daily driver, 128K context

Why it matters A stable multilingual baseline with long context and first-class support across local toolchains.

Specs

• Dense 8B decoder-only
• Official 128K context support
• Instruction-tuned and base variants
• Llama license with open weights

Recommended quantization and VRAM Common GGUF builds and Ollama recipes exist. Typical setup: Q4_K_M or Q5_K_M for systems with 12–16 GB VRAM, Q6_K for 24 GB and above.

2) Meta Llama 3.2-1B/3B — edge-class, 128K context, on-device friendly

Why it matters Small models that accept 128K tokens and can run acceptably on CPUs and iGPUs when properly quantized, ideal for laptops and mini-PCs.

Specs

• 1B and 3B instruction-tuned models
• 128K context confirmed by Meta
• Strong compatibility with llama.cpp GGUF and LM Studio runtime stacks

Recommended quantization and VRAM Works well on CPU/CUDA/Vulkan/Metal/ROCm stacks. Use Q4_K_M for tight RAM budgets on small devices.

3) Qwen3-14B / 32B — Apache-2.0, dense and MoE variants

Why it matters A broad family under Apache-2.0 with dense and sparse (MoE) offerings, strong tool use and multilingual ability, and active community GGUF ports.

Specs

• 14B and 32B dense checkpoints, with long-context variants
• Modern tokenizer and rapid ecosystem updates

Recommended quantization and VRAM Start at Q4_K_M for 14B on 12 GB cards; move to Q5 or Q6 when you have 24 GB or more.

4) DeepSeek-R1-Distill-Qwen-7B — compact reasoning model

Why it matters Distilled from R1-style reasoning traces to deliver step-by-step quality at a 7B scale, suitable for math and coding on modest VRAM.

Specs

• 7B dense
• Long-context variants available via conversions
• Widely available GGUF builds from F32 to Q4_K_M

Recommended quantization and VRAM Try Q4_K_M for 8–12 GB VRAM; Q5 or Q6 when you have 16–24 GB.

5) Google Gemma 2-9B / 27B — efficient dense models with explicit 8K context

Why it matters Good quality-for-size and predictable quantization behavior. The 9B variant is an excellent mid-range local model.

Specs

• Dense 9B and 27B
• Explicit 8K context window
• Open weights under Gemma terms

Recommended quantization and VRAM 9B at Q4_K_M runs on many 12 GB cards. Do not assume longer contexts than documented without checking the model card.

6) Mixtral 8×7B (SMoE) — Apache-2.0 sparse MoE workhorse

Why it matters Mixture-of-Experts gives inference throughput benefits by selecting ~2 experts per token at runtime. Good choice when you have ≥24–48 GB VRAM or multi-GPU setups.

Specs

• 8 experts of 7B each, sparse activation
• Apache-2.0 license
• Mature GGUF conversions and Ollama recipes

Recommended hardware Best leveraged on multi-GPU or high VRAM single-GPU systems to exploit throughput advantage.

7) Microsoft Phi-4-mini-3.8B — small and long-context

Why it matters A realistic small-footprint reasoning model with 128K context and grouped-query attention. Solid choice for CPU and iGPU boxes and latency-sensitive tools.

Specs

• 3.8B dense
• 128K context documented in the model card
• SFT/DPO alignment and large vocab

Recommended quantization and VRAM Use Q4_K_M on systems with ≤8–12 GB VRAM for responsive performance.

8) Microsoft Phi-4-Reasoning-14B — mid-size reasoning tuned model

Why it matters A 14B reasoning-tuned variant that often outperforms generic 13–15B baselines on chain-of-thought tasks.

Specs

• Dense 14B
• Context length depends on distribution; some builds list 32K

Recommended quantization and VRAM For 24 GB VRAM, Q5_K_M or Q6_K is comfortable. Mixed-precision non-GGUF runners may require more memory.

9) Yi-1.5-9B / 34B — Apache-2.0 bilingual family

Why it matters Competitive English and Chinese performance under a permissive Apache-2.0 license. The 9B is a viable alternative to Gemma 2-9B.

Specs

• Dense models with 4K, 16K, and 32K variants
• Open weights and active HF repositories

Recommended quantization and VRAM For 9B use Q4 or Q5 on 12–16 GB GPUs.

10) InternLM 2 / 2.5-7B / 20B — research oriented, math tuned branches

Why it matters A research-friendly series with math-tuned branches. 7B is practical locally while 20B approaches mid-range dense capabilities.

Specs

• Dense 7B and 20B
• Chat, base, and math variants widely available
• GGUF conversions and Ollama packs common

Recommended deployment patterns Use the 7B variants for on-device experiments and 20B when you have higher VRAM capacity.

Practical recommendations

• Match model context to your use case: prefer long-context models for document understanding and short-window dense models for predictable latency.
• Standardize on GGUF and llama.cpp for portability, and layer Ollama or LM Studio for convenience and hardware offload.
• Size quantization from Q4 to Q6 according to your memory budget and performance needs.
• Pay attention to licenses: Apache-2.0 and clearly documented open weights reduce legal friction for local deployments.

Choosing by context plus license plus hardware path will get you farther than chasing leaderboard numbers alone.