Local AI Advisor

Can Desktop PC · Intel Arc Pro B60 24GB · 64 GB RAM run GLM-4.7-Flash (30B-A3B)?

24 GB VRAM + 64 GB RAM · 456 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: Q4_K_M (17 GB of weights) on LM Studio. Generation: 70–120 tok/s, leaving about 4.7 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 WebUIExcellentQ4_K_M70–120 tok/s≈1 s8KDetails
Coding questionswith ContinueExcellentQ4_K_M65–110 tok/s≈8 s16KDetails
Coding in a repositorywith AiderComfortableQ4_K_M50–85 tok/s≈25 s32KDetails
Agentic codingwith OpenCodeComfortableQ4_K_M45–75 tok/s≈40 s32KDetails
Long documents (64K)with Open WebUIComfortableQ4_K_M30–50 tok/s≈2 min64KDetails

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 Q4_K_M 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:Q4_K_M

Downloads 18.31 GB (Q4_K_M). 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.