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Can the Intel Arc A380 run Mistral Nemo 12B Instruct?

36Basic quality (D) · 36 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.

Yes, it runs well (offloads to RAM)

Runs well (offloads to RAM). At Q2_K it needs about 7.1 GB of 6.0 GB usable, with an estimated speed of 18.6 tok/s.

Best quant
Q2_K
Generation speed
18.6 tok/s
Prompt processing
310 tok/s
First token (1K prompt)
3.3 s
Largest safe context
—

Best app to run it on the Intel Arc A380

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 unsloth/Mistral-Nemo-Instruct-2407-GGUF:Q2_K
  • LM Studio Splits the model between GPU memory and system RAM, which this setup needs.
  • Ollama 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 unsloth/Mistral-Nemo-Instruct-2407-GGUF:Q2_K
  • LM Studio Splits the model between GPU memory and system RAM, which this setup needs.
  • Ollama Splits the model between GPU memory and system RAM, which this setup needs.

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 Mistral Nemo 12B Instruct

Recommended for the Intel Arc A380: Q2_K. 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 mistral-nemo:12b-instruct-2407-q2_K

Quantized files · GGUF

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

Download the Q2_K file (opens in a new tab)

huggingface.co/unsloth/Mistral-Nemo-Instruct-2407-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Mistral-Nemo-Instruct-2407-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/mistralai/Mistral-Nemo-Instruct-2407 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/mistralai/mistral-inference (opens in a new tab)

Mistral AI’s official inference code.

git clone https://github.com/mistralai/mistral-inference

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

How it’s rated

Quality 36Basic quality (D) · 36 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.
How capable the model itself is at Q2_K, 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 A380: 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.16.4 / 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.10.2 / 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 score51.6 / 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 Intel Arc A380

Quant Memory needed Available Speed Runs Quality Download
Q2_K 7.1 GB 6.0 GB 18.6 tok/s Runs welloffloads to RAM 36Basic quality (D) · 36 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. GGUF (opens in a new tab)
Q3_K_M 8.2 GB 6.0 GB 13.0 tok/s Slowheavy RAM offload 43Fair quality (C) · 43 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 9.7 GB 6.0 GB 9.2 tok/s Slowheavy RAM offload 49Fair quality (C) · 49 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 48/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 10.9 GB 6.0 GB 7.2 tok/s Slowheavy RAM offload 50Fair quality (C) · 50 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 48/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 12.2 GB 6.0 GB 5.8 tok/s Slowheavy RAM offload 51Fair quality (C) · 51 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 15.2 GB 6.0 GB 4.0 tok/s Slowheavy RAM offload 52Fair quality (C) · 52 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 26.6 GB 6.0 GB — Won't run 52Fair quality (C) · 52 of 100: how capable the model is at F16, whatever it runs on. Match score 7/100 (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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