Run Llama 3 70B locally
Llama 3 70B is Meta's flagship open-weight model — one of the best general-purpose local LLMs you can run at home if you have the hardware for it. The catch has always been 'if you have the hardware for it.' At 70 billion parameters, it needs serious memory. That's exactly the gap HiveBear was built for: instead of telling you to buy a $3,000 GPU, the hive lets you pool what you've got with what your neighbors have and run it together.
$ hivebear run llama-3-70bHiveBear will profile your hardware, pick the right quantization for your pool, and fall back to the hive if your machine can't carry it alone.
Hardware: running it alone
On your own, you need either a serious workstation or an M-series Mac with a lot of unified memory. Most laptops and mid-range gaming PCs can't touch it alone.
Even with aggressive quantization (Q4_K_M is the usual sweet spot), you're looking at ~40 GB for the model weights plus headroom for the KV cache. A 16 GB laptop will OOM immediately.
Hardware: running it on the hive
A 16 GB MacBook + a 32 GB gaming PC + a 16 GB mini PC = ~64 GB pooled memory, enough for Llama 3 70B at Q4 with room to spare.
Pipeline parallelism splits the model's layers across machines. Each machine only holds its share. The hive's profiler figures out the split automatically based on each peer's hardware — you don't have to tune anything by hand.
Things to know
Real gotchas from the hive. No sales pitch.
- →First load is slow. The 40 GB download takes a while on any connection. Subsequent runs are instant from disk cache.
- →Pipeline parallelism adds latency per token proportional to the number of hops. Two-machine splits feel great, five-machine splits start to feel noticeable. The sweet spot is usually 2-4 peers.
- →If a peer drops mid-response, the hive will try to re-route. In practice you'll see a brief pause rather than a crash, but it's not zero-interruption.
- →Context window quickly becomes the memory bottleneck. Long conversations eat KV cache faster than you'd expect — plan memory budget accordingly.
What Llama 3 70B is great at
General-purpose chat, reasoning, code, long-form writing. If you want one 'daily driver' local model and your hive can fit it, Llama 3 70B is a fantastic pick.
If this isn't the one, try these instead
- →Llama 3 8B — runs on almost anything alone, fast, still very capable.
- →Mixtral 8x7B — mixture-of-experts, ~47B params, often comparable quality with less active compute.
- →DeepSeek R1 — stronger reasoning performance, heavier hardware requirements.
- →Qwen 2.5 72B — similar size, often outperforms Llama 3 on non-English tasks.
Give it a run on your hive
Free, open-source, no sign-up. The hive helps when your machine can't carry it alone.
