Local AI Advisor

Can Desktop PC · Ryzen 9 9950X (no GPU) · 64 GB DDR5 run Llama 3.3 70B Instruct?

64 GB RAM, no GPU · Llama 3.3 70B Instruct has 70.6B parameters.

Short answer

It loads, but it's too slow even for chat.

Best quantization for chat: Q5_K_M (47 GB of weights) on llama.cpp (llama-server). Generation: 0.7–1.2 tok/s, leaving about 0.8 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.

WorkloadVerdictQuantGenerationFirst replyContext
Chatwith Open WebUITechnically RunsQ5_K_M0.7–1.2 tok/s≈30 s8KDetails
Coding questionswith ContinueTechnically RunsQ4_K_M0.8–1.4 tok/s≈3 min16KDetails
Coding in a repositorywith AiderTechnically RunsQ3_K_M0.9–1.5 tok/s≈13 min32KDetails
Agentic codingwith OpenCodeTechnically RunsQ3_K_M0.9–1.5 tok/s≈19 min32KDetails
Long documents (64K)with Open WebUIDoes Not Fit————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 Q5_K_M build with a 8K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Llama-3.3-70B-Instruct-GGUF:Q5_K_M

Downloads 49.95 GB (Q5_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=8192 before `ollama serve` for long prompts.

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