Model benchmarks
Qwen 2.5 Coder & DeepSeek-R1 Distills: The Best Local LLMs for 8GB and 16GB GPUs
As open-weight model architectures mature in late 2026, two model families have established themselves as the undisputed daily drivers for local AI enthusiasts: Qwen 2.5 Coder and the DeepSeek-R1 Distillations. Unlike cloud APIs with recurring per-token bills and privacy trade-offs, both families can be downloaded in GGUF format and run entirely offline on consumer GPUs.
Why Qwen 2.5 Coder and DeepSeek-R1 Dominate Local AI
For months, running AI locally meant compromising on intelligence compared to commercial frontier APIs. However, recent distillation and instruction-tuning breakthroughs have shifted the paradigm. Qwen 2.5 Coder (14B and 32B) scores competitively with proprietary models on SWE-bench and HumanEval, while DeepSeek-R1 Distills bring step-by-step chain-of-thought mathematical and logic reasoning to consumer hardware.
Exact VRAM Requirements by GPU Tier
Here is how both model families map to popular consumer graphics cards at practical quantization levels (Q4_K_M and Q5_K_M, with a standard 4,096 to 8,192 token context window):
| Model Size | Recommended Quant | VRAM Required | Target GPU (Fits in VRAM) | Expected Speed |
|---|---|---|---|---|
| Qwen 2.5 Coder 7B | Q5_K_M | ~6.2 GB | RTX 4060 8GB, RTX 3070 | 45–60 tok/s |
| DeepSeek-R1 Distill 7B | Q5_K_M | ~6.3 GB | RTX 4060 8GB, RTX 3060 12GB | 40–55 tok/s |
| Qwen 2.5 Coder 14B | Q4_K_M | ~9.8 GB | RTX 3060 12GB, RTX 4070 | 35–45 tok/s |
| DeepSeek-R1 Distill 14B | Q4_K_M | ~10.1 GB | RTX 3060 12GB, RTX 4070 | 30–40 tok/s |
| Qwen 2.5 Coder 14B (High) | Q8_0 | ~15.6 GB | RTX 4060 Ti 16GB, RTX 5070 Ti | 28–38 tok/s |
| Qwen 2.5 Coder 32B | Q4_K_M | ~20.5 GB | RTX 3090 24GB, RTX 4090 | 25–35 tok/s |
| DeepSeek-R1 Distill 32B | Q4_K_M | ~20.8 GB | RTX 3090 24GB, RTX 4090 | 22–30 tok/s |
The 12 GB Sweet Spot: Why Qwen 14B is the Coding Champion
If you own a card with 12 GB of VRAM — such as the popular NVIDIA RTX 3060 12 GB or the RTX 4070 — Qwen 2.5 Coder 14B is arguably the best balance of code generation quality and speed available today. At Q4_K_M, the entire model and an 8K context buffer fit into roughly 10 GB of video memory, leaving sufficient headroom for display output and desktop rendering without spilling into slow system RAM.
How to Run Them in Under Two Minutes
Both models are natively indexed in Ollama and LM Studio. You can test them immediately from your terminal:
# Run Qwen 2.5 Coder (7B or 14B)
ollama run qwen2.5-coder:7b
ollama run qwen2.5-coder:14b
# Run DeepSeek-R1 Reasoning Distill
ollama run deepseek-r1:7b
ollama run deepseek-r1:14bBottom Line
You no longer need an expensive multi-GPU server to run intelligent, private AI. An 8 GB graphics card runs 7B models at full conversational speeds; a 12 GB or 16 GB card comfortably runs 14B models capable of professional-grade software development; and a 24 GB card brings frontier 32B reasoning models directly to your desktop.
- Search all verified GGUF models in our directory.
- Check your GPU's exact VRAM limits.
- Read our complete GPU buyer's guide for local LLM inference.