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Can the NVIDIA GeForce RTX 4070 SUPER run Apertus v1.5 8B?

45Fair quality (C) · 45 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q6_K it needs about 9.6 GB of 12.0 GB usable, with an estimated speed of 48.2 tok/s.

Best quant
Q6_K
Generation speed
48.2 tok/s
Prompt processing
2,872 tok/s
First token (1K prompt)
0.4 s
Largest safe context
26K

Best app to run it on the NVIDIA GeForce RTX 4070 SUPER

On Windows

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/swiss-ai/Apertus-v1.5-8B

On Linux

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/swiss-ai/Apertus-v1.5-8B
  • vLLM For one person, llama.cpp-based apps are about as fast and use smaller GGUF files.
  • SGLang For one person, llama.cpp-based apps are about as fast and use smaller GGUF files.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Apertus v1.5 8B

Original weights · Hugging Face

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

huggingface.co/swiss-ai/Apertus-v1.5-8B (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

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

How it’s rated

Quality 45Fair quality (C) · 45 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q6_K, 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 Runs great
How well it runs on the NVIDIA GeForce RTX 4070 SUPER: 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.20.5 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 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 score75.5 / 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 NVIDIA GeForce RTX 4070 SUPER

Quant Memory needed Available Speed Runs Quality Download
Q2_K 5.8 GB 12.0 GB 114 tok/s Runs great 32Basic quality (D) · 32 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 6.6 GB 12.0 GB 89.9 tok/s Runs great 38Basic quality (D) · 38 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 7.7 GB 12.0 GB 69.5 tok/s Runs great 43Fair quality (C) · 43 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 8.6 GB 12.0 GB 57.4 tok/s Runs great 44Fair quality (C) · 44 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 9.6 GB 12.0 GB 48.2 tok/s Runs great 45Fair quality (C) · 45 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 11.7 GB 12.0 GB 35.4 tok/s Runs welltight fit 46Fair quality (C) · 46 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. All files (opens in a new tab)
F16 20.1 GB 12.0 GB 4.1 tok/s Slowheavy RAM offload 46Fair quality (C) · 46 of 100: how capable the model is at F16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. 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

Quick answers

How much memory does Apertus v1.5 8B need on the RTX 4070 SUPER 12GB?

At Q6_K it needs about 9.6 GB. The RTX 4070 SUPER 12GB has 12.0 GB usable.

How fast is Apertus v1.5 8B on the RTX 4070 SUPER 12GB?

About 48.2 tok/s at Q6_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Apertus v1.5 8B should I use on the RTX 4070 SUPER 12GB?

Q6_K. 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.

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