Local LLM Model Comparison for Budget Hardware
Five open-source models tested on real consumer hardware — a Mac Mini M2 (16GB), a Windows PC with an RTX 3060 (12GB), and a laptop with 8GB unified memory. We measured tokens-per-second, prompt quality, memory footprint, and which model you should pick for your hardware.
Why model selection matters more than hardware
The biggest mistake in local LLM deployment is picking the wrong model for your hardware. Run a 70B model on 16GB of RAM and you get 0.3 tokens per second — unusable. Run a 7B quantized model on the same machine and you get 30–50 tok/s, fast enough for real-time chat and document summarization.
We tested five models across three hardware tiers. All models were run through llama.cpp with Q4_K_M quantization unless noted, using the same prompt set: summarization, code generation, creative writing, and structured output.
If you haven't set up your local LLM environment yet, start with our budget deployment guide for content teams — it covers hardware choices and initial setup.
The five models benchmarked
| Model | Size (params) | Q4_K_M RAM needed | Strengths |
|---|---|---|---|
| Phi-4 | 14B | ~9 GB | Reasoning, code, math — punches above its weight |
| Llama 3.1 | 8B | ~6 GB | Balanced general purpose, great instruction following |
| Mistral (Nemo) | 12B | ~8 GB | Fast inference, strong multilingual, good at long context |
| Qwen 2.5 | 7B / 14B | ~5 GB / ~9 GB | Strong coding & math, excellent structured output |
| DeepSeek Coder V2 Lite | 16B | ~11 GB | Code generation specialist, best-in-class for dev tasks |
All models tested as GGUF quantized files from Hugging Face. llama.cpp version: b4398. We used default inference settings (temperature 0.7, top-p 0.9, context length 4096).
Benchmark results: Tokens per second
| Hardware | Phi-4 14B | Llama 3.1 8B | Mistral Nemo 12B | Qwen 2.5 7B | DeepSeek Coder 16B |
|---|---|---|---|---|---|
| Mac Mini M2 (16GB) | 22 tok/s | 48 tok/s | 30 tok/s | 52 tok/s | 18 tok/s |
| RTX 3060 (12GB) | 35 tok/s | 68 tok/s | 42 tok/s | 72 tok/s | 28 tok/s |
| Laptop 8GB (CPU only) | 4 tok/s | 12 tok/s | 7 tok/s | 14 tok/s | — (OOM) |
Key takeaway: On 16GB Mac Mini, Phi-4 delivers the best quality-to-speed ratio. On RTX 3060, Qwen 2.5 7B is the fastest while maintaining solid output quality. On 8GB laptops, Llama 3.1 8B or Qwen 2.5 7B are your only practical options.
Quality comparison across task types
Speed is only half the story. We rated output quality on a 1–5 scale across four task types:
| Task | Phi-4 | Llama 3.1 | Mistral Nemo | Qwen 2.5 7B | DeepSeek Coder |
|---|---|---|---|---|---|
| Summarization | 4.5 | 4.0 | 4.0 | 3.5 | 3.0 |
| Code generation | 4.5 | 3.5 | 3.5 | 4.5 | 5.0 |
| Creative writing | 3.5 | 4.0 | 4.5 | 3.0 | 2.5 |
| Structured output (JSON) | 4.0 | 4.0 | 3.5 | 4.5 | 4.5 |
Phi-4 is the all-rounder — strong at summarization and code, decent at everything else. Mistral Nemo wins on creative writing and handles long documents well. DeepSeek Coder is the specialist — unmatched at code but mediocre elsewhere. For teams sharing a single model, Phi-4 is the best compromise; for multi-purpose use, see our small team deployment guide for running multiple models on a shared server.
Recommendations by hardware tier
8GB RAM (MacBook Air, budget laptops)
Stick with Llama 3.1 8B (Q4_K_M) or Qwen 2.5 7B. These run at 10–14 tok/s on CPU, usable for chat, summarization, and light coding. Avoid anything above 8B parameters — you'll hit swap and get sub-5 tok/s.
16GB RAM (Mac Mini M2/M3, mid-range PCs)
Your sweet spot is Phi-4 14B (Q4_K_M) — 22 tok/s on Apple Silicon, best quality-per-watt on the market. For teams that need code generation, add Qwen 2.5 7B as a secondary model. You can serve both from the same machine using Open WebUI's model routing.
24GB+ / GPU (RTX 3060+, M-series Pro/Max)
You can run Qwen 2.5 14B at Q4_K_M (35+ tok/s on GPU) or Mistral Nemo at Q6_K for better creative quality. If your primary task is coding, DeepSeek Coder V2 Lite at Q4_K_M fits in 12GB GPU memory and delivers best-in-class code.
For more on multi-user setups with these models, check our small team deployment guide.
Quantization: how to run bigger models on less hardware
Quantization is the single most impactful technique for running local LLMs on budget hardware. By reducing each weight from 16-bit to 4-bit, you cut memory usage by 75% with surprisingly little quality loss.
For prompt engineering tips that work consistently across quantized models, see our prompt engineering guide for developers.
Which model should you pick?
If you only read one paragraph: Phi-4 14B (Q4_K_M) is the best single model for budget hardware in 2026. It fits in 16GB RAM, runs at 20+ tok/s on Apple Silicon, and scores 4+ on every quality metric except creative writing. If your team needs creative output, add Mistral Nemo as a secondary model. If you're doing heavy code generation, DeepSeek Coder is worth the memory cost.
For document analysis workflows that combine LLMs with retrieval, see our RAG document analysis guide — it benchmarks the same models in a RAG pipeline.