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Can the Intel N100 / N95 NAS (8 GB) run Kimi Linear 48B-A3B?

51Fair quality (C) · 51 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking.

No, it doesn’t fit

No. Kimi Linear 48B-A3B needs about 20.7 GB even at Q2_K; the Intel N100 / N95 NAS (8 GB) has 5.2 GB usable.

Download Kimi Linear 48B-A3B

Recommended: Q2_K. 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 Q2_K file (opens in a new tab)

huggingface.co/bartowski/moonshotai_Kimi-Linear-48B-A3B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/bartowski/moonshotai_Kimi-Linear-48B-A3B-Instruct-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/MoonshotAI/Kimi-Linear (opens in a new tab)

git clone https://github.com/MoonshotAI/Kimi-Linear

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 51Fair quality (C) · 51 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 6/100 (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 Won't run
How well it runs on the Intel N100 / N95 NAS (8 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.7.0 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.0.0 / 15
Match score7.0 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every quantization on the Intel N100 / N95 NAS (8 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 20.7 GB 5.2 GB — Won't run 51Fair quality (C) · 51 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 25.2 GB 5.2 GB — Won't run 62Good quality (B) · 62 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 31.0 GB 5.2 GB — Won't run 70Great quality (A) · 70 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 36.0 GB 5.2 GB — Won't run 71Great quality (A) · 71 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 41.4 GB 5.2 GB — Won't run 73Great quality (A) · 73 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 53.1 GB 5.2 GB — Won't run 74Great quality (A) · 74 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
F16 99.3 GB 5.2 GB — Won't run 74Great quality (A) · 74 of 100: how capable the model is at F16, whatever it runs on. Match score 9/100 (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.

Hardware that can run it

Models that do run on the Intel N100 / N95 NAS (8 GB)

Quick answers

How much memory does Kimi Linear 48B-A3B need on the Intel N100 / N95 NAS 8GB?

Even at Q2_K it needs about 20.7 GB; the Intel N100 / N95 NAS 8GB has 5.2 GB usable.

What can the Intel N100 / N95 NAS 8GB run instead?

Gemma 4 E2B, Ling 3.0 Tiny (8B-A1.3B) and Unlimited OCR 2 (3B-A0.6B) run best on it; the page lists more, each with its speed.

What hardware can run Kimi Linear 48B-A3B?

M2 Ultra 192GB, M4 Max 128GB and AMD Ryzen AI Max+ 395 128GB run it; the page lists more, each with its speed.

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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