Local AI Advisor

Can Desktop PC · GeForce RTX 5090 · 64 GB RAM run Qwen2.5-Coder 7B Instruct?

32 GB VRAM + 64 GB RAM · 1792 GB/s memory bandwidth · Qwen2.5-Coder 7B Instruct has 7.6B parameters.

Short answer

Yes, excellent for chat and acceptable or better for every workload we test.

Best quantization for chat: Q6_K (6.0 GB of weights) on Ollama. Generation: 140–200 tok/s, leaving about 24 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 WebUIExcellentQ6_K140–200 tok/sunder 1 s8KDetails
Coding questionswith ContinueComfortableQ6_K140–190 tok/sunder 1 s16KDetails
Coding in a repositorywith AiderAcceptableQ8_0100–140 tok/s≈2 s32KDetails
Agentic codingwith OpenCodeAcceptableQ8_0100–130 tok/s≈2 s32KDetails
Long documents (64K)with Open WebUIAcceptableQ6_K120–160 tok/s≈4 s32KDetails

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 Q6_K build with a 32K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen2.5-Coder-7B-Instruct-GGUF:Q6_K

Downloads 6.25 GB (Q6_K). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=32768 before `ollama serve` for long prompts.

Different tool, context size or workload?

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