Deploying GLM‑4.7‑Flash Quantized Model Locally on a Single RTX 4090

This guide walks through downloading the AWQ‑4bit quantized GLM‑4.7‑Flash model, upgrading vLLM, building a custom Docker image, and launching the model on two RTX 4090 GPUs with tuned parameters to avoid OOM, while sharing practical tips and observed performance.

AWQ-4bitDockerGLM-4.7-Flash

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Deploying GLM‑4.7‑Flash Quantized Model Locally on a Single RTX 4090

Programmer's Advance

Jan 21, 2026 · Artificial Intelligence

Why GLM‑4.7‑Flash Delivers 70B‑Level Performance with Only 30B Parameters

GLM‑4.7‑Flash, released by Zhipu AI on Jan 20 2026, uses a Mixture‑of‑Experts (MoE) backbone and a Multi‑Latent Attention (MLA) mechanism to achieve near‑70B model quality with just 30 B total and 3 B active parameters, running on a single 24 GB GPU or even a Mac, while remaining fully open‑source and free to use.

AI Model BenchmarkGLM-4.7-FlashMixture of Experts

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Why GLM‑4.7‑Flash Delivers 70B‑Level Performance with Only 30B Parameters

AI Insight Log

Jan 20, 2026 · Artificial Intelligence

Is GLM-4.7-Flash the New 30B‑Level LLM King? Open‑Source and Ollama‑Ready

GLM‑4.7‑Flash, a 30B‑parameter MoE LLM released as fully open‑source and free, delivers 30B‑class performance across six benchmarks, runs locally with a single Ollama command, and offers a faster cloud‑hosted version with modest token‑based pricing, though hardware costs still apply.

Anthropic APIGLM-4.7-FlashMixture of Experts

0 likes · 7 min read

Is GLM-4.7-Flash the New 30B‑Level LLM King? Open‑Source and Ollama‑Ready

Deploying GLM‑4.7‑Flash Quantized Model Locally on a Single RTX 4090

Why GLM‑4.7‑Flash Delivers 70B‑Level Performance with Only 30B Parameters

Is GLM-4.7-Flash the New 30B‑Level LLM King? Open‑Source and Ollama‑Ready

Deploying GLM‑4.7‑Flash Quantized Model Locally on a Single RTX 4090