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Can the Intel Arc Pro B70 run Kolibri 1 (78B-A3.5B)?

76Great quality (A) · 76 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (offloads to RAM)

Runs well (offloads to RAM). At Q3_K_M it needs about 40.6 GB of 32.0 GB usable, with an estimated speed of 70.1 tok/s.

Best quant
Q3_K_M
Generation speed
70.1 tok/s
Prompt processing
5,006 tok/s
First token (1K prompt)
0.3 s
Largest safe context
—

Best app to run it on the Intel Arc Pro B70

On Windows

llama.cpp

Command line + local API · runs on llama.cpp

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Gives you direct control of how many layers sit on the GPU, the best way to squeeze a model that doesn’t quite fit.
  • Supports Intel Arc through SYCL and Vulkan.
llama-server -hf bartowski/Kolibri-1-GGUF:Q3_K_M
  • LM Studio Splits the model between GPU memory and system RAM, which this setup needs.
  • Jan Splits the model between GPU memory and system RAM, which this setup needs.

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Gives you direct control of how many layers sit on the GPU, the best way to squeeze a model that doesn’t quite fit.
  • Supports Intel Arc through SYCL and Vulkan.
llama-server -hf bartowski/Kolibri-1-GGUF:Q3_K_M
  • LM Studio Splits the model between GPU memory and system RAM, which this setup needs.
  • Jan Splits the model between GPU memory and system RAM, which this setup needs.

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 Intel Arc Pro B70: Q3_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 Q3_K_M file (opens in a new tab)

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

ollama run hf.co/bartowski/Kolibri-1-GGUF:Q3_K_M

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 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 76Great quality (A) · 76 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q3_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 welloffloads to RAM
How well it runs on the Intel Arc Pro B70: 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.34.4 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.10.0 / 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 score74.4 / 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 Arc Pro B70

Quant Memory needed Available Speed Runs Quality Download
Q2_K 33.6 GB 32.0 GB 126 tok/s Runs welloffloads to RAM 63Good quality (B) · 63 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 40.6 GB 32.0 GB 70.1 tok/s Runs welloffloads to RAM 76Great quality (A) · 76 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 50.0 GB 32.0 GB 43.2 tok/s Slowheavy RAM offload 86Excellent quality (A+) · 86 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 57.8 GB 32.0 GB — Won't run 88Excellent quality (A+) · 88 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q6_K 66.4 GB 32.0 GB — Won't run 90Excellent quality (A+) · 90 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q8_0 85.2 GB 32.0 GB — Won't run 91Excellent quality (A+) · 91 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
F16 159 GB 32.0 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 Arc Pro B70 32GB?

At Q3_K_M it needs about 40.6 GB. The Arc Pro B70 32GB has 32.0 GB usable.

How fast is Kolibri 1 (78B-A3.5B) on the Arc Pro B70 32GB?

About 70.1 tok/s at Q3_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 Arc Pro B70 32GB?

Q3_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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