Chat fit
Llama 3.2 3B
Good fit at 8GB VRAM · 3.7 GB planning memory · 2.7 GB minimum load
3B parameters · Meta · Llama
Hardware-fit local LLM recommendations
Best Local LLMs for Your Hardware and Use Case
AIPCFit defines the best local LLM as the best practical fit for your PC, GPU memory, quantization choice, and intended workload. These recommendations use deterministic catalog data for model size, GGUF quantization, memory requirements, compatibility, and use-case tags.
Start with hardware
Use the checker when you are searching for the best local LLM for my PC and need a direct fit result.
Then choose workload
Filter local LLM models by existing use-case tags such as chat, coding, reasoning, RAG, and lightweight.
Confirm limits
Fit can change with context length, runtime overhead, GPU offload, software, and quantization.
By use case
Best local LLMs by use case
Each set below starts from models already tagged for that use case in the catalog, then selects practical fits from the existing VRAM guide logic. These are hardware-fit recommendations, not universal benchmark rankings.
Chat
General assistant and conversation models tagged for chat in the catalog.
Q4_K_M
Chat fit
Phi-3.5 Mini
Good fit at 8GB VRAM · 4.2 GB planning memory · 3.2 GB minimum load
3.8B parameters · Microsoft · Phi
Chat fit
Phi-4 Mini
Good fit at 8GB VRAM · 4.2 GB planning memory · 3.2 GB minimum load
3.8B parameters · Microsoft · Phi
Chat fit
Gemma 3 4B
Good fit at 8GB VRAM · 4.5 GB planning memory · 3.5 GB minimum load
4B parameters · Google · Gemma
Coding fit
Phi-3.5 Mini
Good fit at 8GB VRAM · 4.2 GB planning memory · 3.2 GB minimum load
3.8B parameters · Microsoft · Phi
Coding fit
Phi-4 Mini
Good fit at 8GB VRAM · 4.2 GB planning memory · 3.2 GB minimum load
3.8B parameters · Microsoft · Phi
Coding fit
DeepSeek-R1 Distill Qwen 7B
Good fit at 8GB VRAM · 6.1 GB planning memory · 5.1 GB minimum load
7B parameters · DeepSeek · DeepSeek
Coding fit
Mistral 7B
Good fit at 8GB VRAM · 6.1 GB planning memory · 5.1 GB minimum load
7B parameters · Mistral AI · Mistral
Reasoning
Reasoning-tagged models where AIPCFit can identify a practical local fit.
Reasoning fit
DeepSeek-R1 Distill Qwen 7B
Good fit at 8GB VRAM · 6.1 GB planning memory · 5.1 GB minimum load
7B parameters · DeepSeek · DeepSeek
Reasoning fit
Gemma 3 12B
Tight at 8GB VRAM · 9.9 GB planning memory · 8.4 GB minimum load
12B parameters · Google · Gemma
Reasoning fit
DeepSeek-R1 Distill Qwen 14B
Good fit at 12GB VRAM · 10.9 GB planning memory · 9.3 GB minimum load
14B parameters · DeepSeek · DeepSeek
Reasoning fit
Phi-4
Good fit at 12GB VRAM · 10.9 GB planning memory · 9.3 GB minimum load
14B parameters · Microsoft · Phi
RAG
Models tagged for retrieval-augmented generation and knowledge workflows.
RAG fit
Llama 3.2 3B
Good fit at 8GB VRAM · 3.7 GB planning memory · 2.7 GB minimum load
3B parameters · Meta · Llama
RAG fit
Gemma 3 4B
Good fit at 8GB VRAM · 4.5 GB planning memory · 3.5 GB minimum load
4B parameters · Google · Gemma
RAG fit
Qwen3 4B
Good fit at 8GB VRAM · 4.5 GB planning memory · 3.5 GB minimum load
4B parameters · Alibaba Cloud · Qwen
RAG fit
Llama 3.1 8B
Good fit at 8GB VRAM · 6.6 GB planning memory · 5.6 GB minimum load
8B parameters · Meta · Llama
Lightweight
Smaller local models tagged for compact or lower-memory setups.
Lightweight fit
Llama 3.2 3B
Good fit at 8GB VRAM · 3.7 GB planning memory · 2.7 GB minimum load
3B parameters · Meta · Llama
Lightweight fit
Phi-3.5 Mini
Good fit at 8GB VRAM · 4.2 GB planning memory · 3.2 GB minimum load
3.8B parameters · Microsoft · Phi
Lightweight fit
Phi-4 Mini
Good fit at 8GB VRAM · 4.2 GB planning memory · 3.2 GB minimum load
3.8B parameters · Microsoft · Phi
Lightweight fit
Gemma 3 4B
Good fit at 8GB VRAM · 4.5 GB planning memory · 3.5 GB minimum load
4B parameters · Google · Gemma
By hardware tier
Best local LLMs by VRAM tier
These summaries use representative dedicated GPU memory tiers already supported by AIPCFit. Use the linked VRAM pages for the full model list, including detailed fit status and memory planning. This page only highlights practical matches for common searches such as best local LLM for 8GB VRAM, 12GB VRAM, 16GB VRAM, and 24GB VRAM.
Hardware tier
8GB VRAM
- Llama 3.2 3B · Q4_K_M · Good fit
- Phi-3.5 Mini · Q4_K_M · Good fit
- Phi-4 Mini · Q4_K_M · Good fit
- Gemma 3 4B · Q4_K_M · Good fit
Hardware tier
12GB VRAM
- Llama 3.2 3B · Q4_K_M · Good fit
- Phi-3.5 Mini · Q4_K_M · Good fit
- Phi-4 Mini · Q4_K_M · Good fit
- Gemma 3 4B · Q4_K_M · Good fit
Hardware tier
16GB VRAM
- Llama 3.2 3B · Q4_K_M · Good fit
- Phi-3.5 Mini · Q4_K_M · Good fit
- Phi-4 Mini · Q4_K_M · Good fit
- Gemma 3 4B · Q4_K_M · Good fit
Hardware tier
24GB VRAM
- Llama 3.2 3B · Q4_K_M · Good fit
- Phi-3.5 Mini · Q4_K_M · Good fit
- Phi-4 Mini · Q4_K_M · Good fit
- Gemma 3 4B · Q4_K_M · Good fit
Hardware tier
32GB+ VRAM
- Llama 3.2 3B · Q4_K_M · Good fit
- Phi-3.5 Mini · Q4_K_M · Good fit
- Phi-4 Mini · Q4_K_M · Good fit
- Gemma 3 4B · Q4_K_M · Good fit
Methodology
What best means here
These are hardware-fit recommendations. They are not universal model-quality rankings, benchmark leaderboards, fastest-model claims, or intelligence rankings.
A practical local fit depends on model file size, quantization, load-memory headroom, context length, runtime memory behavior, GPU offloading, system RAM, unified memory, drivers, and the software used to run the model.
Model quality and speed can vary by workload and runtime. Use the fit checker for your exact PC, then compare model pages, compatibility pages, and VRAM guides before choosing.