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Can the Intel Arc A770 16GB run Apriel 1.5 15B Thinker?

68Good quality (B) · 68 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q4_K_M it needs about 12.1 GB of 16.0 GB usable, with an estimated speed of 35.3 tok/s.

Best quant
Q4_K_M
Generation speed
35.3 tok/s
Prompt processing
1,455 tok/s
First token (1K prompt)
0.7 s
Largest safe context
27K

Best app to run it on the Intel Arc A770 16GB

On Windows

llama.cpp

Command line + local API · runs on llama.cpp

  • Supports Intel Arc through SYCL and Vulkan.
  • Usually the first to support new model architectures.
llama-server -hf unsloth/Apriel-1.5-15b-Thinker-GGUF:Q4_K_M
  • LM Studio Point-and-click: search, download and chat in one window.
  • Jan Point-and-click: search, download and chat in one window.

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Supports Intel Arc through SYCL and Vulkan.
  • Usually the first to support new model architectures.
llama-server -hf unsloth/Apriel-1.5-15b-Thinker-GGUF:Q4_K_M
  • LM Studio Point-and-click: search, download and chat in one window.
  • Jan Point-and-click: search, download and chat in one window.

Want an agent? Apriel 1.5 15B Thinker 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 Apriel 1.5 15B Thinker

Recommended for the Intel Arc A770 16GB: Q4_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 Q4_K_M file (opens in a new tab)

huggingface.co/unsloth/Apriel-1.5-15b-Thinker-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Apriel-1.5-15b-Thinker-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/ServiceNow-AI/Apriel-1.5-15b-Thinker (opens in a new tab)

No sign-in needed.

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 68Good quality (B) · 68 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_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 Intel Arc A770 16GB: 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.30.8 / 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.14.1 / 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 score84.9 / 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 A770 16GB

Quant Memory needed Available Speed Runs Quality Download
Q2_K 9.0 GB 16.0 GB 58.0 tok/s Runs great 50Fair quality (C) · 50 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 10.3 GB 16.0 GB 45.7 tok/s Runs great 60Good quality (B) · 60 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 12.1 GB 16.0 GB 35.3 tok/s Runs great 68Good quality (B) · 68 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 13.6 GB 16.0 GB 29.2 tok/s Runs well 69Good quality (B) · 69 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 15.2 GB 16.0 GB 24.5 tok/s Runs welltight fit 71Great quality (A) · 71 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 18.8 GB 16.0 GB 9.2 tok/s Slowoffloads to RAM 72Great quality (A) · 72 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 32.7 GB 16.0 GB 2.0 tok/s Very slowheavy RAM offload 72Great quality (A) · 72 of 100: how capable the model is at F16, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (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

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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