Nemotron 3.5 Bolt E4B on Annapurna Labs Alpine AL324 NAS (16 GB)

Can the Annapurna Labs Alpine AL324 NAS (16 GB) run Nemotron 3.5 Bolt E4B?

Barely: it runs very slowly

Very slow. At Q8_0 it needs about 10.2 GB of 12.0 GB usable, with an estimated speed of 0.5 tok/s.

Best quant: Q8_0
Generation speed: 0.5 tok/s
Prompt processing: 2.2 tok/s
First token (1K prompt): 460.8 s
Largest safe context: 64K

Best app to run it on the Annapurna Labs Alpine AL324 NAS (16 GB)

On Linux on ARM boards

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

One command with Ollama’s own build of this model.
Serves an OpenAI-compatible API that other apps and coding tools can use.

ollama run nemotron-3.5-bolt:e4b-q8_0

llama.cpp Usually the first to support new model architectures.

Want an agent? Nemotron 3.5 Bolt E4B supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download Nemotron 3.5 Bolt E4B

Recommended for the Annapurna Labs Alpine AL324 NAS (16 GB): Q8_0. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run nemotron-3.5-bolt:e4b-q8_0

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q8_0 file (opens in a new tab)

huggingface.co/unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF:Q8_0

Original weights · Hugging Face

Full-precision files from NVIDIA, for fine-tuning, vLLM, Transformers.

huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16 (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q8_0, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Very slow: How well it runs on the Annapurna Labs Alpine AL324 NAS (16 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

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 qualityEditorial quality tier of the model, scaled by the quantization or precision used.	31.5 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	1.7 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	15.0 / 15
Match score	73.2 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the Annapurna Labs Alpine AL324 NAS (16 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	4.9 GB	12.0 GB	1.6 tok/s	Very slow	49Fair quality (C) · 49 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q2_K_XL) (opens in a new tab)
`Q3_K_M`	5.6 GB	12.0 GB	1.3 tok/s	Very slow	58Good quality (B) · 58 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.	GGUF (UD-Q3_K_XL) (opens in a new tab)
`Q4_K_M`	6.6 GB	12.0 GB	1.0 tok/s	Very slow	66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q4_K_XL) (opens in a new tab)
`Q5_K_M`	7.4 GB	12.0 GB	0.8 tok/s	Very slow	67Good quality (B) · 67 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q5_K_XL) (opens in a new tab)
`Q6_K`	8.3 GB	12.0 GB	0.7 tok/s	Very slow	69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q6_K_XL) (opens in a new tab)
`Q8_0`	10.2 GB	12.0 GB	0.5 tok/s	Very slow	70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	17.7 GB	12.0 GB	—	Won't run	70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 9/100 (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.

Other hardware that runs it

70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX F16 · 52.9 tok/s · Runs great
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. Intel Arc B580 Q8_0 · 48.4 tok/s · Runs great
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. NVIDIA GeForce RTX 3060 12GB Q8_0 · 50.0 tok/s · Runs great
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 F16 · 59.9 tok/s · Runs great
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. NVIDIA GeForce RTX 4070 SUPER Q8_0 · 70.0 tok/s · Runs great
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 F16 · 64.5 tok/s · Runs great

Quick answers

How much memory does Nemotron 3.5 Bolt E4B need on the Annapurna Labs Alpine AL324 NAS 16GB?

At Q8_0 it needs about 10.2 GB. The Annapurna Labs Alpine AL324 NAS 16GB has 12.0 GB usable.

How fast is Nemotron 3.5 Bolt E4B on the Annapurna Labs Alpine AL324 NAS 16GB?

About 0.5 tok/s at Q8_0, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Nemotron 3.5 Bolt E4B should I use on the Annapurna Labs Alpine AL324 NAS 16GB?

Q8_0. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.