Llama 3.1 70B Instruct

Positioning

The 70B that proved local 70B was viable. If you have an existing fine-tune, a deployment locked to this version, or the disk to keep it alongside 3.3, it's still a strong general model. For new installs, Llama 3.3 70B is the better default at the same VRAM cost.

Strengths

• Long-trusted baseline: every benchmark, every fine-tune recipe, every tool-use harness has been calibrated against it.
• License identical to 3.3 — same permissive commercial terms.
• Tool-use behavior is well-understood — the JSON function-calling format Meta documented is rock-solid.

Limitations

• Beaten by Llama 3.3 70B on instruction following, math, and multi-turn coherence at the same VRAM.
• Reasoning is generic vs the R1 Distill family which is dramatically stronger on math/code planning.
• Knowledge cutoff late-2023 — visibly stale.

Real-world performance on RTX 4090

• Q4_K_M (39 GB) — partial offload: 22–28 tok/s decode, TTFT 350–500 ms
• Q5_K_M (47 GB) — heavy offload: 9–14 tok/s
• Q8_0 (70 GB) — workstation only

Should you run this locally?

Yes, for existing fine-tuned variants you depend on, or as a known-quantity baseline for evaluation harnesses. No, for new deployments — pick Llama 3.3 70B at the same VRAM, or DeepSeek R1 Distill 70B if reasoning matters.

How it compares

• vs Llama 3.3 70B → 3.3 wins outright at the same memory. No reason to start with 3.1 70B for new work.
• vs Qwen 2.5 72B → roughly equal capability; Qwen has stronger multilingual, Llama has more mature ecosystem.
• vs Mixtral 8x22B → Llama 3.1 70B is more memory-efficient (39 GB Q4 vs ~84 GB) and matches or beats Mixtral on most tasks.

Run this yourself

ollama pull llama3.1:70b-instruct-q4_K_M
ollama run llama3.1:70b-instruct-q4_K_M

Settings: Q4_K_M GGUF, 8192 ctx, --n-gpu-layers 65 of 81, RTX 4090 + 64 GB RAM