Can Desktop PC · GeForce RTX 5070 Ti · 32 GB RAM run Llama 3.1 8B Instruct?

16 GB VRAM + 32 GB RAM · 896 GB/s memory bandwidth · Llama 3.1 8B Instruct has 8B parameters.

Short answer

Yes, excellent for chat; borderline for coding in a repository.

Best quantization for chat: Q6_K (6.3 GB of weights) on Ollama. Generation: 80–110 tok/s, leaving about 6.8 GB free for your other apps.

How it rates for each workload

Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.

Workload	Verdict	Quant	Generation	First reply	Context
Chatwith Open WebUI	Excellent	Q6_K	80–110 tok/s	under 1 s	8K	Details
Coding questionswith Continue	Acceptable	Q6_K	75–100 tok/s	≈1 s	16K	Details
Coding in a repositorywith Aider	Borderline	Q8_0	55–70 tok/s	≈3 s	32K	Details
Agentic codingwith OpenCode	Borderline	Q8_0	50–70 tok/s	≈4 s	32K	Details
Long documents (64K)with Open WebUI	Comfortable	Q4_K_M	50–65 tok/s	≈15 s	64K	Details

Speeds are estimates calibrated against measured benchmarks on similar hardware. “First reply” is the time to read the workload’s opening prompt. How we calculate

Get it running

Copy-paste commands for the Q4_K_M build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Llama-3.1-8B-Instruct-GGUF:Q4_K_M

Downloads 4.92 GB (Q4_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=65536 before `ollama serve` for long prompts.

Other models for Desktop PC · GeForce RTX 5070 Ti · 32 GB RAM

The best-rated models for chat on this machine.

Everything Desktop PC · GeForce RTX 5070 Ti · 32 GB RAM can run

Cheapest machines for Llama 3.1 8B Instruct

Hardware rated comfortable or better for chat, lowest price first.

Different tool, context size or workload?

These pages use typical settings. The full check models your exact setup.

Open weights (restricted)Check my exact setup