Mistral-NeMo-Minitron 8B Instruct on Intel Core Ultra 200H (Arc 140T) (32 GB)

Can the Intel Core Ultra 200H (Arc 140T) (32 GB) run Mistral-NeMo-Minitron 8B Instruct?

Yes, it runs well

Runs well. At Q4_K_M it needs about 8.5 GB of 18.2 GB usable, with an estimated speed of 11.4 tok/s.

Best quant: Q4_K_M
Generation speed: 11.4 tok/s
Prompt processing: 602 tok/s
First token (1K prompt): 1.7 s
Largest safe context: 8K

Best app to run it on the Intel Core Ultra 200H (Arc 140T) (32 GB)

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 bartowski/Mistral-NeMo-Minitron-8B-Instruct-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 bartowski/Mistral-NeMo-Minitron-8B-Instruct-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.

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-Minitron 8B Instruct

Recommended for the Intel Core Ultra 200H (Arc 140T) (32 GB): 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/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/nvidia/Mistral-NeMo-Minitron-8B-Instruct (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 61/100 (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 well: How well it runs on the Intel Core Ultra 200H (Arc 140T) (32 GB): 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.	18.0 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	8.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	10.8 / 15
Match score	61.8 / 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 Core Ultra 200H (Arc 140T) (32 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	6.8 GB	18.2 GB	18.8 tok/s	Runs well	29Basic quality (D) · 29 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	7.5 GB	18.2 GB	14.8 tok/s	Runs well	35Basic quality (D) · 35 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	8.5 GB	18.2 GB	11.4 tok/s	Runs well	39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	9.4 GB	18.2 GB	9.4 tok/s	Slow	40Fair quality (C) · 40 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	10.3 GB	18.2 GB	7.9 tok/s	Slow	41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	12.3 GB	18.2 GB	5.8 tok/s	Slow	42Fair quality (C) · 42 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`	20.2 GB	18.2 GB	2.3 tok/s	Very slowfar over GPU memory limit	42Fair quality (C) · 42 of 100: how capable the model is at F16, whatever it runs on. Match score 41/100 (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

42Fair quality (C) · 42 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q8_0 · 41.0 tok/s · Runs great
42Fair quality (C) · 42 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER Q8_0 · 49.9 tok/s · Runs great
42Fair quality (C) · 42 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER Q8_0 · 54.7 tok/s · Runs great
42Fair quality (C) · 42 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5070 Ti Q8_0 · 66.6 tok/s · Runs great
42Fair quality (C) · 42 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 Q8_0 · 71.3 tok/s · Runs great
41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER Q6_K · 51.0 tok/s · Runs great

Quick answers

How much memory does Mistral-NeMo-Minitron 8B Instruct need on the Intel Core Ultra 200H (Arc 140T) 32GB?

At Q4_K_M it needs about 8.5 GB. The Intel Core Ultra 200H (Arc 140T) 32GB has 18.2 GB usable.

How fast is Mistral-NeMo-Minitron 8B Instruct on the Intel Core Ultra 200H (Arc 140T) 32GB?

About 11.4 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Mistral-NeMo-Minitron 8B Instruct should I use on the Intel Core Ultra 200H (Arc 140T) 32GB?

Q4_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.