Can Laptop · GeForce RTX 5090 Laptop · 32 GB RAM run Llama 3.1 8B Instruct?

24 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: 60–100 tok/s, leaving about 15 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	60–100 tok/s	under 1 s	8K	Details
Coding questionswith Continue	Acceptable	Q6_K	60–100 tok/s	≈1 s	16K	Details
Coding in a repositorywith Aider	Borderline	Q8_0	40–70 tok/s	≈3 s	32K	Details
Agentic codingwith OpenCode	Borderline	Q8_0	40–65 tok/s	≈4 s	32K	Details
Long documents (64K)with Open WebUI	Comfortable	Q8_0	30–45 tok/s	≈15 s	64K	Details

Speeds are bandwidth-based estimates; no direct benchmark exists for this pair. “First reply” is the time to read the workload’s opening prompt. How we calculate

Get it running

Copy-paste commands for the Q8_0 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:UD-Q8_K_XL

Downloads 10.58 GB (UD-Q8_K_XL). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=65536 before `ollama serve` for long prompts.

Other models for Laptop · GeForce RTX 5090 Laptop · 32 GB RAM

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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