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Can the Intel Arc A770 16GB run SmolLM2 1.7B Instruct?

20Basic quality (D) · 20 of 100: how capable the model is at F16, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At F16 it needs about 6.4 GB of 16.0 GB usable, with an estimated speed of 72.0 tok/s.

Best quant
F16
Generation speed
72.0 tok/s
Prompt processing
12,640 tok/s
First token (1K prompt)
0.3 s
Largest safe context
8K

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 HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF:F16
  • LM Studio Point-and-click: search, download and chat in one window.
  • Ollama Intel GPU support here is experimental (Vulkan).

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 HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF:F16
  • LM Studio Point-and-click: search, download and chat in one window.
  • Ollama Intel GPU support here is experimental (Vulkan).

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 SmolLM2 1.7B Instruct

Recommended for the Intel Arc A770 16GB: F16. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run smollm2:1.7b-instruct-fp16

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF

Original weights · Hugging Face

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

huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/huggingface/smollm (opens in a new tab)

git clone https://github.com/huggingface/smollm

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

How it’s rated

Quality 20Basic quality (D) · 20 of 100: how capable the model is at F16, whatever it runs on. Match score 58/100 (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 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.9.0 / 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.9.3 / 15
Match score58.3 / 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 3.7 GB 16.0 GB 504 tok/s Runs great 14Basic quality (D) · 14 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.
Q3_K_M 3.8 GB 16.0 GB 397 tok/s Runs great 16Basic quality (D) · 16 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.
Q4_K_M 4.1 GB 16.0 GB 307 tok/s Runs great 19Basic quality (D) · 19 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 4.2 GB 16.0 GB 254 tok/s Runs great 19Basic quality (D) · 19 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking.
Q6_K 4.4 GB 16.0 GB 213 tok/s Runs great 19Basic quality (D) · 19 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.
Q8_0 4.8 GB 16.0 GB 156 tok/s Runs great 20Basic quality (D) · 20 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.
F16 6.4 GB 16.0 GB 72.0 tok/s Runs great 20Basic quality (D) · 20 of 100: how capable the model is at F16, whatever it runs on. Match score 58/100 (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

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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