Local AI Advisor

Can Desktop PC · Radeon RX 7900 XT · 32 GB RAM run GLM-4.7-Flash (30B-A3B)?

20 GB VRAM + 32 GB RAM · 800 GB/s memory bandwidth · GLM-4.7-Flash (30B-A3B) has 31.2B parameters (3B active per token).

Short answer

Yes, excellent for chat; borderline for 64K-token documents.

Best quantization for chat: Q3_K_M (14 GB of weights) on Lemonade. Generation: 190–320 tok/s, leaving about 3.5 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 WebUIExcellentQ3_K_M190–320 tok/sunder 1 s8KDetails
Coding questionswith ContinueComfortableQ3_K_M170–290 tok/s≈2 s16KDetails
Coding in a repositorywith AiderAcceptableQ3_K_M130–220 tok/s≈7 s32KDetails
Agentic codingwith OpenCodeAcceptableQ3_K_M120–200 tok/s≈9 s32KDetails
Long documents (64K)with Open WebUIBorderlineQ4_K_M30–50 tok/s≈45 s64KDetails

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 Q3_K_M build with a 32K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/GLM-4.7-Flash-GGUF:Q3_K_M

Downloads 14.61 GB (Q3_K_M). 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.