GLM-4.6 (355B-A32B) on NVIDIA RTX A2000 12GB
Can the NVIDIA RTX A2000 12GB run GLM-4.6 (355B-A32B)?
No, it doesn’t fit
No. GLM-4.6 (355B-A32B) needs about 147 GB even at Q2_K; the NVIDIA RTX A2000 12GB has 12.0 GB usable.
Download GLM-4.6 (355B-A32B)
Recommended: Q2_K. Every other quantization has its own download link in the table below.
Ollama
Ollama only offered this as a cloud model, and has since retired it. To run it locally, use the GGUF files below with llama.cpp or LM Studio.
Quantized files · GGUF
Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.
Download the Q2_K files (opens in a new tab)
huggingface.co/unsloth/GLM-4.6-GGUF (opens in a new tab)
ollama run hf.co/unsloth/GLM-4.6-GGUFOriginal weights · Hugging Face
Full-precision files from Zhipu AI, for fine-tuning, vLLM, Transformers.
Official repo · GitHub
Reference code, inference scripts and docs from Zhipu AI. The weights themselves download from Hugging Face.
github.com/zai-org/GLM-4.5 (opens in a new tab)
git clone https://github.com/zai-org/GLM-4.5How it’s rated
Match score
To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.
| Model quality | 7.9 / 45 | |
|---|---|---|
| Memory fit | 0.0 / 25 | |
| Speed | 0.0 / 15 | |
| Memory use | 0.0 / 15 | |
| Match score | 7.9 / 100 |
Every quantization on the NVIDIA RTX A2000 12GB
| Quant | Memory needed | Available | Speed | Runs | Quality | Download |
|---|---|---|---|---|---|---|
Q2_K | 147 GB | 12.0 GB | — | Won't run | 58Good quality (B) · 58 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking. | GGUF · folder (opens in a new tab) |
Q3_K_M | 179 GB | 12.0 GB | — | Won't run | 70Great quality (A) · 70 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. | GGUF · folder (opens in a new tab) |
Q4_K_M | 222 GB | 12.0 GB | — | Won't run | 79Great quality (A) · 79 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. | GGUF · folder (opens in a new tab) |
Q5_K_M | 258 GB | 12.0 GB | — | Won't run | 81Great quality (A) · 81 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. | GGUF · folder (opens in a new tab) |
Q6_K | 297 GB | 12.0 GB | — | Won't run | 83Great quality (A) · 83 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. | GGUF · folder (opens in a new tab) |
Q8_0 | 382 GB | 12.0 GB | — | Won't run | 84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. | GGUF · folder (opens in a new tab) |
F16 | 718 GB | 12.0 GB | — | Won't run | 84Great quality (A) · 84 of 100: how capable the model is at F16, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. | GGUF · folder (opens in a new tab) Original weights (opens in a new tab) |
Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.
Hardware that can run it
-
79Great quality (A) · 79 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking.
Apple M3 Ultra (512 GB)
-
58Good quality (B) · 58 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.
Apple M3 Ultra (256 GB)
-
58Good quality (B) · 58 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking.
Apple M2 Ultra (192 GB)
-
79Great quality (A) · 79 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking.
AMD Instinct MI350X
-
79Great quality (A) · 79 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking.
Apple M5 Ultra (512 GB)
-
79Great quality (A) · 79 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking.
AMD Instinct MI325X
Models that do run on the NVIDIA RTX A2000 12GB
- 76Great quality (A) · 76 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. K2 Horizon 7B
- 78Great quality (A) · 78 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 12B
- 74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. SenseNova Loop 9B Preview
- 74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. MiMo V2.6 Distill Qwen 9B
- 70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. Nemotron 3.5 Bolt E4B
Quick answers
How much memory does GLM-4.6 (355B-A32B) need on the NVIDIA RTX A2000 12GB?
Even at Q2_K it needs about 147 GB; the NVIDIA RTX A2000 12GB has 12.0 GB usable.
What can the NVIDIA RTX A2000 12GB run instead?
K2 Horizon 7B, Gemma 4 12B and SenseNova Loop 9B Preview run best on it; the page lists more, each with its speed.
What hardware can run GLM-4.6 (355B-A32B)?
M3 Ultra 512GB, M3 Ultra 256GB and M2 Ultra 192GB run it; the page lists more, each with its speed.
Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.