Llama 3.1 8B Instruct

Positioning

The default 8B-class model for anyone who wants a permissive, English-strong, runs-everywhere chat assistant. If you have an RTX 3060 12 GB or anything stronger, this is the model you start with — it's the one the entire local-LLM tutorial ecosystem is calibrated against.

Strengths

• Fits everything: Q4_K_M is 4.6 GB. Runs on a 6 GB card with reduced context, comfortably on 8 GB+, and at full 128K context on a 12 GB+ card with KV cache trimming.
• Instruction following is excellent: handles multi-turn, system prompts, JSON-mode-via-prompt, and tool-call-style outputs without the brittleness Mistral 7B shows.
• Genuinely permissive license: the Llama 3.1 Community License allows commercial use up to 700M MAUs — which is everyone reading this.

Limitations

• Math and code are average, not strong. For coding work, Qwen 2.5 Coder 7B is meaningfully better.
• 128K context is nominal, not real — quality starts degrading past ~32K tokens, and effective recall over very long inputs is weaker than the spec suggests.
• Alignment refusals are noticeable in technical domains (security research, pen-testing tutorials). Hermes-3-8B is a good uncensored alternative on the same base.

Real-world performance on RTX 4090

• Q4_K_M (4.6 GB): 95–115 tok/s decode, TTFT under 80 ms on a 1K prompt
• Q5_K_M (5.6 GB): 88–100 tok/s
• Q8_0 (8.5 GB): 70–82 tok/s — the quality bump over Q5 is small; rarely worth the speed loss

Should you run this locally?

Yes, for general assistant work, summarization, drafting, RAG pipelines, and as the chat model behind tooling/agents that need a fast, predictable backbone. No, for serious code generation (use Qwen 2.5 Coder), heavy reasoning (use QwQ 32B or DeepSeek R1 Distill), or non-English tasks where Qwen 2.5 7B is consistently stronger.

How it compares

• vs Qwen 2.5 7B → Qwen wins on knowledge breadth and multilingual tasks; Llama wins on instruction reliability and ecosystem maturity. Coin flip with the edge to Qwen if you're comfortable using it.
• vs Mistral 7B v0.3 → Llama wins decisively on instruction following and long-context behavior. Mistral 7B is the previous default; there's no reason to start there now.
• vs Phi-3.5 Mini (3.8B) → Llama is far more capable; Phi is the right pick only when VRAM is genuinely tight (sub-6 GB cards).
• vs Llama 3.2 3B → Llama 3.1 8B is materially better at almost everything but uses ~2× the VRAM. The 3B is for VRAM-constrained edge devices.

Run this yourself

ollama pull llama3.1:8b-instruct-q4_K_M
ollama run llama3.1:8b-instruct-q4_K_M

Settings used in the timing range above Quant: Q4_K_M GGUF Context: 8192 (KV cache f16) Backend: llama.cpp via Ollama, CUDA 12.4 GPU: RTX 4090, driver 555.99