GLM-4 9B Chat 1M on Annapurna Labs Alpine AL324 NAS (4 GB)
Can the Annapurna Labs Alpine AL324 NAS (4 GB) run GLM-4 9B Chat 1M?
No, it doesn’t fit
No. GLM-4 9B Chat 1M needs about 5.2 GB even at Q2_K; the Annapurna Labs Alpine AL324 NAS (4 GB) has 2.0 GB usable.
Download GLM-4 9B Chat 1M
Recommended: Q2_K. Every other quantization has its own download link in the table below.
Quantized files · GGUF
Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.
Download the Q2_K file (opens in a new tab)
huggingface.co/bartowski/glm-4-9b-chat-1m-GGUF (opens in a new tab)
ollama run hf.co/bartowski/glm-4-9b-chat-1m-GGUF:Q2_KOriginal weights · Hugging Face
Full-precision files from Zhipu AI, for fine-tuning, vLLM, Transformers.
huggingface.co/zai-org/glm-4-9b-chat-1m (opens in a new tab)
How 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 | 4.5 / 45 | |
|---|---|---|
| Memory fit | 0.0 / 25 | |
| Speed | 0.0 / 15 | |
| Memory use | 0.0 / 15 | |
| Match score | 4.5 / 100 |
Every quantization on the Annapurna Labs Alpine AL324 NAS (4 GB)
| Quant | Memory needed | Available | Speed | Runs | Quality | Download |
|---|---|---|---|---|---|---|
Q2_K | 5.2 GB | 2.0 GB | — | Won't run | 33Basic quality (D) · 33 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (opens in a new tab) |
Q3_K_M | 6.1 GB | 2.0 GB | — | Won't run | 40Fair quality (C) · 40 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (opens in a new tab) |
Q4_K_M | 7.2 GB | 2.0 GB | — | Won't run | 45Fair quality (C) · 45 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (opens in a new tab) |
Q5_K_M | 8.2 GB | 2.0 GB | — | Won't run | 46Fair quality (C) · 46 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (opens in a new tab) |
Q6_K | 9.2 GB | 2.0 GB | — | Won't run | 47Fair quality (C) · 47 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (opens in a new tab) |
Q8_0 | 11.5 GB | 2.0 GB | — | Won't run | 48Fair quality (C) · 48 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (opens in a new tab) |
F16 | 20.4 GB | 2.0 GB | — | Won't run | 48Fair quality (C) · 48 of 100: how capable the model is at F16, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (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
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48Fair quality (C) · 48 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.
NVIDIA GeForce RTX 4070 Ti SUPER
-
48Fair quality (C) · 48 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.
NVIDIA GeForce RTX 4080 SUPER
-
48Fair quality (C) · 48 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.
NVIDIA GeForce RTX 5070 Ti
-
48Fair quality (C) · 48 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.
NVIDIA GeForce RTX 5080
-
48Fair quality (C) · 48 of 100: how capable the model is at F16, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.
NVIDIA GeForce RTX 5090
-
47Fair quality (C) · 47 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.
NVIDIA GeForce RTX 4070 SUPER
Models that do run on the Annapurna Labs Alpine AL324 NAS (4 GB)
- 47Fair quality (C) · 47 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. LFM3 1.7B
- 39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. PaddleOCR-VL 1.6 0.9B
- 39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. MiniCPM-OCR 1.0 1.3B
- 35Basic quality (D) · 35 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. LFM3-VL 2B
- 34Basic quality (D) · 34 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. MiniCPM5 1B
Quick answers
How much memory does GLM-4 9B Chat 1M need on the Annapurna Labs Alpine AL324 NAS 4GB?
Even at Q2_K it needs about 5.2 GB; the Annapurna Labs Alpine AL324 NAS 4GB has 2.0 GB usable.
What can the Annapurna Labs Alpine AL324 NAS 4GB run instead?
LFM3 1.7B, PaddleOCR-VL 1.6 0.9B and MiniCPM-OCR 1.0 1.3B run best on it; the page lists more, each with its speed.
What hardware can run GLM-4 9B Chat 1M?
RTX 4070 Ti SUPER 16GB, RTX 4080 SUPER 16GB and RTX 5070 Ti 16GB 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.