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Can the NVIDIA RTX 5000 Ada Generation run Zamba2 2.7B Instruct?

22Basic quality (D) · 22 of 100: how capable the model is at F16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At F16 it needs about 7.9 GB of 32.0 GB usable, with an estimated speed of 62.4 tok/s.

Best quant
F16
Generation speed
62.4 tok/s
Prompt processing
17,675 tok/s
First token (1K prompt)
0.3 s
Largest safe context
4K

Best app to run it on the NVIDIA RTX 5000 Ada Generation

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/Zyphra/Zamba2-2.7B-instruct

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/Zyphra/Zamba2-2.7B-instruct
  • 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 Zamba2 2.7B Instruct

Original weights · Hugging Face

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

huggingface.co/Zyphra/Zamba2-2.7B-instruct (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 22Basic quality (D) · 22 of 100: how capable the model is at F16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at F16, 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 RTX 5000 Ada Generation: 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.9.9 / 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.5.7 / 15
Match score55.6 / 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 RTX 5000 Ada Generation

Quant Memory needed Available Speed Runs Quality Download
Q2_K 3.6 GB 32.0 GB 437 tok/s Runs great 15Basic quality (D) · 15 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 49/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 3.9 GB 32.0 GB 344 tok/s Runs great 18Basic quality (D) · 18 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 4.2 GB 32.0 GB 266 tok/s Runs great 20Basic quality (D) · 20 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 4.5 GB 32.0 GB 220 tok/s Runs great 21Basic quality (D) · 21 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 4.8 GB 32.0 GB 184 tok/s Runs great 21Basic quality (D) · 21 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 5.4 GB 32.0 GB 135 tok/s Runs great 22Basic quality (D) · 22 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 7.9 GB 32.0 GB 62.4 tok/s Runs great 22Basic quality (D) · 22 of 100: how capable the model is at F16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 4K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does Zamba2 2.7B Instruct need on the NVIDIA RTX 5000 Ada Generation 32GB?

At F16 it needs about 7.9 GB. The NVIDIA RTX 5000 Ada Generation 32GB has 32.0 GB usable.

How fast is Zamba2 2.7B Instruct on the NVIDIA RTX 5000 Ada Generation 32GB?

About 62.4 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Zamba2 2.7B Instruct should I use on the NVIDIA RTX 5000 Ada Generation 32GB?

F16. 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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