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Can the NVIDIA A800 80GB run Kolibri 1 (78B-A3.5B)?

88Excellent quality (A+) · 88 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q5_K_M it needs about 60.2 GB of 80.0 GB usable, with an estimated speed of 190 tok/s.

Best quant
Q5_K_M
Generation speed
190 tok/s
Prompt processing
16,231 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Best app to run it on the NVIDIA A800 80GB

On Linux

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Point-and-click: search, download and chat in one window.

Open Kolibri 1 (78B-A3.5B) in LM Studio

  • llama.cpp Usually the first to support new model architectures.
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Point-and-click: search, download and chat in one window.

Open Kolibri 1 (78B-A3.5B) in LM Studio

  • llama.cpp Usually the first to support new model architectures.
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

Want an agent? Kolibri 1 (78B-A3.5B) 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, 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 Kolibri 1 (78B-A3.5B)

Recommended for the NVIDIA A800 80GB: Q5_K_M. 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 Q5_K_M files (opens in a new tab)

huggingface.co/bartowski/Kolibri-1-GGUF (opens in a new tab)

ollama run hf.co/bartowski/Kolibri-1-GGUF

Original weights · Hugging Face

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

huggingface.co/Aleph-Alpha/Kolibri-1-BF16 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Aleph Alpha. The weights themselves download from Hugging Face.

github.com/Aleph-Alpha/aleph-alpha-inference (opens in a new tab)

Aleph Alpha's inference package with the vLLM plugin needed to serve Kolibri in vLLM.

git clone https://github.com/Aleph-Alpha/aleph-alpha-inference

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

How it’s rated

Quality 88Excellent quality (A+) · 88 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q5_K_M, 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 A800 80GB: 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.39.7 / 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 score94.7 / 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 NVIDIA A800 80GB

Quant Memory needed Available Speed Runs Quality Download
Q2_K 36.0 GB 80.0 GB 249 tok/s Runs great 63Good quality (B) · 63 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 43.0 GB 80.0 GB 229 tok/s Runs great 76Great quality (A) · 76 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 52.4 GB 80.0 GB 207 tok/s Runs great 86Excellent quality (A+) · 86 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 60.2 GB 80.0 GB 190 tok/s Runs great 88Excellent quality (A+) · 88 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q6_K 68.8 GB 80.0 GB 175 tok/s Runs welltight fit 90Excellent quality (A+) · 90 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q8_0 87.6 GB 80.0 GB 71.5 tok/s Runs welloffloads to RAM 91Excellent quality (A+) · 91 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
F16 161 GB 80.0 GB — Won't run 91Excellent quality (A+) · 91 of 100: how capable the model is at F16, whatever it runs on. Match score 12/100 (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

Quick answers

How much memory does Kolibri 1 (78B-A3.5B) need on the NVIDIA A800 80GB?

At Q5_K_M it needs about 60.2 GB. The NVIDIA A800 80GB has 80.0 GB usable.

How fast is Kolibri 1 (78B-A3.5B) on the NVIDIA A800 80GB?

About 190 tok/s at Q5_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Kolibri 1 (78B-A3.5B) should I use on the NVIDIA A800 80GB?

Q5_K_M. 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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