Native LLMs matured quick in 2025: open-weight households like Llama 3.1 (128K context size (ctx)), Qwen3 (Apache-2.0, dense + MoE), Gemma 2 (9B/27B, 8K ctx), Mixtral 8×7B (Apache-2.0 SMoE), and Phi-4-mini (3.8B, 128K ctx) now ship dependable specs and first-class native runners (GGUF/llama.cpp, LM Studio, Ollama), making on-prem and even laptop computer inference sensible when you match context size and quantization to VRAM. This information lists the ten most deployable choices by license readability, secure GGUF availability, and reproducible efficiency traits (params, context size (ctx), quant presets).
Prime 10 Native LLMs (2025)
1) Meta Llama 3.1-8B — strong “every day driver,” 128K context
Why it issues. A secure, multilingual baseline with lengthy context and first-class help throughout native toolchains.
Specs. Dense 8B decoder-only; official 128K context; instruction-tuned and base variants. Llama license (open weights). Frequent GGUF builds and Ollama recipes exist. Typical setup: Q4_K_M/Q5_K_M for ≤12-16 GB VRAM, Q6_K for ≥24 GB.
2) Meta Llama 3.2-1B/3B — edge-class, 128K context, on-device pleasant
Why it issues. Small fashions that also take 128K tokens and run acceptably on CPUs/iGPUs when quantized; good for laptops and mini-PCs.
Specs. 1B/3B instruction-tuned fashions; 128K context confirmed by Meta. Works nicely by way of llama.cpp GGUF and LM Studio’s multi-runtime stack (CPU/CUDA/Vulkan/Steel/ROCm).
3) Qwen3-14B / 32B — open Apache-2.0, sturdy tool-use & multilingual
Why it issues. Broad household (dense+MoE) underneath Apache-2.0 with lively neighborhood ports to GGUF; broadly reported as a succesful basic/agentic “every day driver” regionally.
Specs. 14B/32B dense checkpoints with long-context variants; trendy tokenizer; fast ecosystem updates. Begin at Q4_K_M for 14B on 12 GB; transfer to Q5/Q6 when you might have 24 GB+. (Qwen)
4) DeepSeek-R1-Distill-Qwen-7B — compact reasoning that matches
Why it issues. Distilled from R1-style reasoning traces; delivers step-by-step high quality at 7B with broadly obtainable GGUFs. Glorious for math/coding on modest VRAM.
Specs. 7B dense; long-context variants exist per conversion; curated GGUFs cowl F32→Q4_K_M. For 8–12 GB VRAM strive Q4_K_M; for 16–24 GB use Q5/Q6.
5) Google Gemma 2-9B / 27B — environment friendly dense; 8K context (express)
Why it issues. Sturdy quality-for-size and quantization conduct; 9B is a good mid-range native mannequin.
Specs. Dense 9B/27B; 8K context (don’t overstate); open weights underneath Gemma phrases; broadly packaged for llama.cpp/Ollama. 9B@Q4_K_M runs on many 12 GB playing cards.
6) Mixtral 8×7B (SMoE) — Apache-2.0 sparse MoE; value/perf workhorse
Why it issues. Combination-of-Consultants throughput advantages at inference: ~2 specialists/token chosen at runtime; nice compromise when you might have ≥24–48 GB VRAM (or multi-GPU) and wish stronger basic efficiency.
Specs. 8 specialists of 7B every (sparse activation); Apache-2.0; instruct/base variants; mature GGUF conversions and Ollama recipes.
7) Microsoft Phi-4-mini-3.8B — small mannequin, 128K context
Why it issues. Real looking “small-footprint reasoning” with 128K context and grouped-query consideration; strong for CPU/iGPU containers and latency-sensitive instruments.
Specs. 3.8B dense; 200k vocab; SFT/DPO alignment; mannequin card paperwork 128K context and coaching profile. Use Q4_K_M on ≤8–12 GB VRAM.
8) Microsoft Phi-4-Reasoning-14B — mid-size reasoning (examine ctx per construct)
Why it issues. A 14B reasoning-tuned variant that’s materially higher for chain-of-thought-style duties than generic 13–15B baselines.
Specs. Dense 14B; context varies by distribution (mannequin card for a standard launch lists 32K). For twenty-four GB VRAM, Q5_K_M/Q6_K is comfy; mixed-precision runners (non-GGUF) want extra.
9) Yi-1.5-9B / 34B — Apache-2.0 bilingual; 4K/16K/32K variants
Why it issues. Aggressive EN/zh efficiency and permissive license; 9B is a robust different to Gemma-2-9B; 34B steps towards larger reasoning underneath Apache-2.0.
Specs. Dense; context variants 4K/16K/32K; open weights underneath Apache-2.0 with lively HF playing cards/repos. For 9B use This fall/Q5 on 12–16 GB.
10) InternLM 2 / 2.5-7B / 20B — research-friendly; math-tuned branches
Why it issues. An open sequence with energetic analysis cadence; 7B is a sensible native goal; 20B strikes you towards Gemma-2-27B-class functionality (at larger VRAM).
Specs. Dense 7B/20B; a number of chat/base/math variants; lively HF presence. GGUF conversions and Ollama packs are frequent.
Abstract
In native LLMs, the trade-offs are clear: choose dense fashions for predictable latency and easier quantization (e.g., Llama 3.1-8B with a documented 128K context; Gemma 2-9B/27B with an express 8K window), transfer to sparse MoE like Mixtral 8×7B when your VRAM and parallelism justify larger throughput per value, and deal with small reasoning fashions (Phi-4-mini-3.8B, 128K) because the candy spot for CPU/iGPU containers. Licenses and ecosystems matter as a lot as uncooked scores: Qwen3’s Apache-2.0 releases (dense + MoE) and Meta/Google/Microsoft mannequin playing cards give the operational guardrails (context, tokenizer, utilization phrases) you’ll truly stay with. On the runtime aspect, standardize on GGUF/llama.cpp for portability, layer Ollama/LM Studio for comfort and {hardware} offload, and measurement quantization (This fall→Q6) to your reminiscence funds. Briefly: select by context + license + {hardware} path, not simply leaderboard vibes.
Michal Sutter is a knowledge science skilled with a Grasp of Science in Knowledge Science from the College of Padova. With a strong basis in statistical evaluation, machine studying, and knowledge engineering, Michal excels at remodeling complicated datasets into actionable insights.
