SenseNova 6.8 Code2Video on Intel Atom C3538 / C3558 NAS (128 GB)

Can the Intel Atom C3538 / C3558 NAS (128 GB) run SenseNova 6.8 Code2Video?

Barely: it runs very slowly

Very slow. At Q8_0 it needs about 39.1 GB of 113 GB usable, with an estimated speed of 0.2 tok/s.

Best quant: Q8_0
Generation speed: 0.2 tok/s
Prompt processing: 0.7 tok/s
First token (1K prompt): 1,556.5 s
Largest safe context: 256K

Best app to run it on the Intel Atom C3538 / C3558 NAS (128 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

Usually the first to support new model architectures.

llama-server -hf bartowski/sensenova_SenseNova-6.8-Code2Video-GGUF:Q8_0

Ollama Runs the Hugging Face GGUF with ollama run hf.co/…
KoboldCpp

Want an agent? SenseNova 6.8 Code2Video 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 SenseNova 6.8 Code2Video

Recommended for the Intel Atom C3538 / C3558 NAS (128 GB): Q8_0. 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 Q8_0 file (opens in a new tab)

huggingface.co/bartowski/sensenova_SenseNova-6.8-Code2Video-GGUF (opens in a new tab)

ollama run hf.co/bartowski/sensenova_SenseNova-6.8-Code2Video-GGUF:Q8_0

Original weights · Hugging Face

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

huggingface.co/sensenova/SenseNova-6.8-Code2Video (opens in a new tab)

No sign-in needed.

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 66Good quality (B) · 66 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 63/100 (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 Intel Atom C3538 / C3558 NAS (128 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.	29.7 / 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.	1.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	8.0 / 15
Match score	63.7 / 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 Intel Atom C3538 / C3558 NAS (128 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	21.1 GB	113 GB	0.6 tok/s	Very slow	46Fair quality (C) · 46 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	23.6 GB	113 GB	0.5 tok/s	Very slow	55Good quality (B) · 55 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	26.8 GB	113 GB	0.4 tok/s	Very slow	62Good quality (B) · 62 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.	GGUF (opens in a new tab)
`Q5_K_M`	29.6 GB	113 GB	0.3 tok/s	Very slow	64Good quality (B) · 64 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	32.6 GB	113 GB	0.2 tok/s	Very slow	65Good quality (B) · 65 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	39.1 GB	113 GB	0.2 tok/s	Very slow	66Good quality (B) · 66 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	64.9 GB	113 GB	0.1 tok/s	Very slow	66Good quality (B) · 66 of 100: how capable the model is at F16, whatever it runs on. Match score 68/100 (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.

Other hardware that runs it

65Good quality (B) · 65 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q6_K · 55.8 tok/s · Runs great
62Good quality (B) · 62 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 Q4_K_M · 42.0 tok/s · Runs great
62Good quality (B) · 62 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 Q4_K_M · 45.2 tok/s · Runs great
62Good quality (B) · 62 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX Q4_K_M · 37.1 tok/s · Runs great
55Good quality (B) · 55 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER Q3_K_M · 42.7 tok/s · Runs well (tight fit)
55Good quality (B) · 55 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5070 Ti Q3_K_M · 52.0 tok/s · Runs well (tight fit)

Quick answers

How much memory does SenseNova 6.8 Code2Video need on the Intel Atom C3538 / C3558 NAS 128GB?

At Q8_0 it needs about 39.1 GB. The Intel Atom C3538 / C3558 NAS 128GB has 113 GB usable.

How fast is SenseNova 6.8 Code2Video on the Intel Atom C3538 / C3558 NAS 128GB?

About 0.2 tok/s at Q8_0, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of SenseNova 6.8 Code2Video should I use on the Intel Atom C3538 / C3558 NAS 128GB?

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.