Can Laptop · Ryzen AI 9 HX 370 (Radeon 890M) · 32 GB run Llama 3.1 8B Instruct?

32 GB unified · 120 GB/s memory bandwidth · Llama 3.1 8B Instruct has 8B parameters.

Short answer

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

Best quantization for chat: Q5_K_M (5.5 GB of weights) on Lemonade. Generation: 10–17 tok/s, leaving about 16 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	Comfortable	Q5_K_M	10–17 tok/s	≈3 s	8K	Details
Coding questionswith Continue	Acceptable	Q6_K	8–14 tok/s	≈20 s	16K	Details
Coding in a repositorywith Aider	Borderline	Q8_0	6–10 tok/s	≈60 s	32K	Details
Agentic codingwith OpenCode	Borderline	Q6_K	7–11 tok/s	≈85 s	32K	Details
Long documents (64K)with Open WebUI	Borderline	Q6_K	4.6–8 tok/s	≈5 min	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 Q6_K build with a 16K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Llama-3.1-8B-Instruct-GGUF:Q6_K

Downloads 6.6 GB (Q6_K). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=16384 before `ollama serve` for long prompts.

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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