Local AI Advisor

Can Desktop PC · Radeon AI PRO R9700 · 64 GB RAM run GLM-4.7-Flash (30B-A3B)?

32 GB VRAM + 64 GB RAM · 640 GB/s memory bandwidth · GLM-4.7-Flash (30B-A3B) has 31.2B parameters (3B active per token).

Short answer

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

Best quantization for chat: Q6_K (24 GB of weights) on Lemonade. Generation: 140–180 tok/s, leaving about 5.7 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_K140–180 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellentQ6_K130–170 tok/s≈1 s16KDetails
Coding in a repositorywith AiderComfortableQ6_K110–140 tok/s≈3 s32KDetails
Agentic codingwith OpenCodeComfortableQ6_K100–130 tok/s≈4 s32KDetails
Long documents (64K)with Open WebUIExcellentQ6_K65–90 tok/s≈15 s64KDetails

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 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/GLM-4.7-Flash-GGUF:Q6_K

Downloads 24.69 GB (Q6_K). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=65536 before `ollama serve` for long prompts.

Different tool, context size or workload?

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