Llama 3.1 vs DeepSeek R1: Which Local LLM Wins in 2026?
Quick Comparison
| Llama 3.1 8B | DeepSeek R1 8B | DeepSeek R1 70B | |
|---|---|---|---|
| Speed (RTX 5070) | ~118 t/s | ~100 t/s | ~20 t/s (with offload) |
| Speed (Mac Mini M4 Pro) | ~65 t/s | ~58 t/s | ~10 t/s (64GB) |
| VRAM (Q4) | ~5 GB | ~5 GB | ~40 GB |
| Reasoning quality | Good | Excellent | Outstanding |
| Chat / creative writing | Excellent | Good | Good |
| Response time | Fast | Slow (thinks first) | Very slow |
| Best for | Daily use | Hard problems | Research / accuracy |
How DeepSeek R1 Is Different
DeepSeek R1 uses chain-of-thought (CoT) reasoning — before producing an answer, it generates an internal monologue visible in
When to Use Llama 3.1
- ▸General chat and Q&A — fast, fluent, and context-aware
- ▸Code generation and completion — strong on Python, JS, SQL
- ▸Summarization and rewriting — reliable on long documents
- ▸Creative writing — better at narrative and tone than R1
- ▸Anything where response speed matters more than deep reasoning
When to Use DeepSeek R1
- ▸Math and algebra problems — significantly more accurate than Llama 3.1
- ▸Multi-step logic puzzles — R1's chain-of-thought prevents reasoning errors
- ▸Complex code debugging — traces through logic systematically
- ▸Research tasks where one wrong assumption ruins the output
- ▸Anywhere you would re-run a query 3× to get a reliable answer
Hardware Requirements
Both Llama 3.1 8B and DeepSeek R1 8B require the same hardware: approximately 5 GB VRAM at Q4 quantization. Any GPU with 8 GB+ VRAM runs both. The Mac Mini M4 (16 GB) runs both comfortably. The practical difference is output token budget: R1 may generate 2000+ thinking tokens before answering, so sustained inference throughput matters more. An RTX 5070 at 100+ t/s generates R1's thinking tokens in seconds; a CPU-only system at 16 t/s may take 2+ minutes per response.
Running Both with Ollama
# Pull both models
ollama pull llama3.1:8b
ollama pull deepseek-r1:8b
# Switch between them instantly
ollama run llama3.1:8b # Fast chat
ollama run deepseek-r1:8b # Reasoning tasks
# Check which is loaded in VRAM
ollama psVerdict
Llama 3.1 and DeepSeek R1 are complementary, not competing. Pull both. Use Llama 3.1 as your daily driver for speed and fluency. Switch to DeepSeek R1 when you need reliability on hard problems. With Ollama, switching takes one command and models load in under 10 seconds on hardware with fast NVMe storage.
Frequently Asked Questions
Q1Is DeepSeek R1 better than Llama 3.1 for coding?
For debugging and tracing complex logic errors: R1 is better — its chain-of-thought catches reasoning mistakes. For code generation speed and general coding assistance: Llama 3.1 8B is faster and often good enough. For production codebases with complex requirements, try both and compare outputs.
Q2What hardware runs DeepSeek R1 70B locally?
DeepSeek R1 70B at Q4 quantization requires approximately 40 GB. The Mac Mini M4 Pro with 64 GB unified memory is the primary consumer option — it runs R1 70B at ~10 t/s. On Windows/Linux, you'd need a multi-GPU setup or a workstation with 48+ GB VRAM. The R1 8B or 14B variants are more practical for single-GPU consumer hardware.
Q3Does DeepSeek R1 work with Ollama?
Yes. Pull with `ollama pull deepseek-r1:8b` (or :14b, :32b, :70b). The thinking process appears in <think> tags before the answer. Ollama handles all GPU acceleration automatically — Metal on Mac, CUDA on NVIDIA, ROCm on AMD Linux.
Q4Why is DeepSeek R1 so much slower than Llama 3.1?
R1 generates 'thinking' tokens before the answer — an internal chain-of-thought that can be 500–2000 tokens long. At 100 t/s, that adds 5–20 seconds before the actual answer starts. The tradeoff is accuracy: those thinking steps catch errors that Llama 3.1 would miss. For simple tasks, the thinking is wasteful. For hard tasks, it's the whole point.
Q5Can I use both Llama 3.1 and DeepSeek R1 in Open WebUI?
Yes. Open WebUI connected to Ollama shows all installed models in a dropdown. Switch between Llama 3.1 8B and DeepSeek R1 8B mid-conversation or per-chat. Both use the same chat interface — no separate installation needed.
Q6Which model is better for privacy-sensitive tasks?
Both are equally private — they run fully locally with no data sent to external servers. Llama 3.1 was trained by Meta; DeepSeek R1 by DeepSeek AI (China-based). For privacy, what matters is local execution, not origin — both models run 100% on your hardware with network disabled.
Q7What is the best quantization for Llama 3.1 8B locally?
Q4_K_M is the standard recommendation: ~5 GB, minimal quality loss (~3-5% vs fp16), runs on any 8 GB GPU. Q5_K_M offers slightly better quality at ~6 GB — worth it if you have 12 GB VRAM to spare. Q8_0 is near-lossless at ~8 GB — use it if your GPU has 12 GB and you want maximum quality without going to full fp16.
Q8Is Llama 3.1 or DeepSeek R1 better for creative writing?
Llama 3.1 is better for creative writing. Its training emphasizes fluency, narrative coherence, and stylistic variety. DeepSeek R1's chain-of-thought reasoning is optimized for accuracy on structured problems — it tends to produce more analytical, less creative prose. For fiction, poetry, and marketing copy, Llama 3.1 8B is the better choice.