Llama 3 vs DeepSeek locally: which open model to run
Both are strong open-weight families you can run locally. The split is simple: DeepSeek's reasoning models think before answering (a visible chain-of-thought pass that costs tokens and latency), while Llama 3 answers directly. Pick based on whether your task rewards deliberation.
Where each wins
| Task | Better fit | Why |
|---|---|---|
| Multi-step maths, logic, hard code | DeepSeek reasoning | The think pass materially raises accuracy |
| Chat, drafting, summarisation, RAG answers | Llama 3 | Faster, cheaper per answer, no reasoning overhead |
| Function/tool calling in an agent loop | Llama 3 (instruct) | Predictable latency; reasoning tokens complicate loops |
| Tight latency budget (<1 s to first useful output) | Llama 3 | DeepSeek spends seconds "thinking" first |
Running cost is not the same
Same question, same hardware:
Llama 3 70B → ~250 output tokens
DeepSeek reasoning → ~250 answer tokens + 800–2000 "thinking" tokens
That 3–8× token multiplier is real VRAM (KV cache) and real wall-clock time. On a memory-bound local setup it is the dominant cost.
Hardware fit
Both quantise well. A 4-bit 70B-class model needs ~40–45 GB — a 48 GB dual-GPU rig or a 64 GB+ unified-memory Mac. The smaller variants (7–14B) run on a single 24 GB card; DeepSeek's distilled reasoning models in that range are the interesting option — reasoning quality on consumer hardware.
The rule
Run Llama 3 instruct as your default local model. Add a DeepSeek reasoning model as a second endpoint and route to it only for tasks where a wrong answer is expensive and a few seconds of latency is acceptable. Do not make a reasoning model your chat model.