Chatglm3 6B — Hardware Requirements & GPU Compatibility
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ChatGLM3-6B is the third generation of Zhipu AI's (now Z.ai) open bilingual Chinese-English chat model, built on the GLM architecture at around 6.2 billion parameters. Compared with its predecessors it adds native support for function calling, a code interpreter, and agent-style tasks through a newly designed prompt format, alongside a stronger base model, ChatGLM3-6B-Base, trained on more diverse data. A companion long-context variant, ChatGLM3-6B-32K, was released alongside it. Its modest size lets it run on a single consumer GPU. Context length is 8,192 tokens. The code is released under Apache 2.0, but the model weights use a separate Model License that is free for academic research and allows commercial use only after completing Zhipu's registration questionnaire. It was published in October 2023, and has since been superseded by the GLM-4 series.
69.4K downloads
1.2K likesSpecifications
- Publisher
- Z.ai
- Family
- GLM
- Parameters
- 6.2B
- Architecture
- ChatGLMModel
- Context Length
- 8,192 tokens
- Release Date
- 2023-10-25
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HuggingFace
How Much VRAM Does Chatglm3 6B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.9 GB | — | 2.65 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3 GB | — | 2.73 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 3.4 GB | — | 3.04 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 4.1 GB | — | 3.75 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 4.9 GB | — | 4.45 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 5.7 GB | — | 5.15 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 6.9 GB | — | 6.24 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Chatglm3 6B?
Q4_K_M · 4.1 GBChatglm3 6B (Q4_K_M) requires 4.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Chatglm3 6B?
Q4_K_M · 4.1 GB59 devices with unified memory can run Chatglm3 6B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Chatglm3 6B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Chatglm3 6B need?
Chatglm3 6B requires 4.1 GB of VRAM at Q4_K_M, or 13.7 GB at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 6.2B × 4.8 bits ÷ 8 = 3.7 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M4.1 GB- What's the best quantization for Chatglm3 6B?
For Chatglm3 6B, Q4_K_M (4.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (4.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.9 GB.
VRAM requirement by quantization
Q2_K2.9 GBQ3_K_L3.5 GBQ4_K_M ★4.1 GBQ5_K_S4.7 GBQ5_K_M4.9 GBFP1613.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Chatglm3 6B on a Mac?
Chatglm3 6B requires at least 2.9 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Chatglm3 6B locally?
Yes — Chatglm3 6B can run locally on consumer hardware. At Q4_K_M quantization it needs 4.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Chatglm3 6B?
At Q4_K_M, Chatglm3 6B can reach ~1165 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~159 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 4.1 × 0.65 = ~1262 tok/s
Estimated speed at Q4_K_M (4.1 GB)
~1262 tok/s~159 tok/s~1262 tok/s~1165 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Chatglm3 6B?
At Q4_K_M, the download is about 3.75 GB. The full-precision FP16 version is 12.49 GB. The smallest option (Q2_K) is 2.65 GB.
- Which GPUs can run Chatglm3 6B?
52 consumer GPUs can run Chatglm3 6B at Q4_K_M (4.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Chatglm3 6B?
59 devices with unified memory can run Chatglm3 6B at Q4_K_M (4.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.