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Can the Intel Core Ultra 200V (Arc 140V) (16 GB) run Flint (40B-A2.5B)?

63Good quality (B) · 63 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking.

No, it doesn’t fit

No. Flint (40B-A2.5B) needs about 17.5 GB even at Q2_K; the Intel Core Ultra 200V (Arc 140V) (16 GB) has 9.1 GB usable.

Download Flint (40B-A2.5B)

Recommended: 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 flint:40b

Quantized files · GGUF

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

Download the Q2_K (UD-Q2_K_XL) file (opens in a new tab)

huggingface.co/unsloth/flint-GGUF (opens in a new tab)

ollama run hf.co/unsloth/flint-GGUF:UD-Q2_K_XL

Original weights · Hugging Face

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

huggingface.co/RecursiveSI/flint (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/recursive-oss/rec (opens in a new tab)

Recursive's own local inference engine, linked from the model card.

git clone https://github.com/recursive-oss/rec

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

How it’s rated

Quality 63Good quality (B) · 63 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 8/100 (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 Core Ultra 200V (Arc 140V) (16 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.8.5 / 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.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 score8.5 / 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 Core Ultra 200V (Arc 140V) (16 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 17.5 GB 9.1 GB — Won't run 63Good quality (B) · 63 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 21.1 GB 9.1 GB — Won't run 75Great quality (A) · 75 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF (UD-Q3_K_XL) (opens in a new tab)
Q4_K_M 25.8 GB 9.1 GB — Won't run 85Excellent quality (A+) · 85 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. GGUF (UD-Q4_K_XL) (opens in a new tab)
Q5_K_M 29.8 GB 9.1 GB — Won't run 87Excellent quality (A+) · 87 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. GGUF (UD-Q5_K_XL) (opens in a new tab)
Q6_K 34.2 GB 9.1 GB — Won't run 89Excellent quality (A+) · 89 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. GGUF (UD-Q6_K_XL) (opens in a new tab)
Q8_0 43.7 GB 9.1 GB — Won't run 90Excellent quality (A+) · 90 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 81.1 GB 9.1 GB — Won't run 90Excellent quality (A+) · 90 of 100: how capable the model is at F16, whatever it runs on. Match score 12/100 (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 Core Ultra 200V (Arc 140V) (16 GB)

Quick answers

How much memory does Flint (40B-A2.5B) need on the Intel Core Ultra 200V (Arc 140V) 16GB?

Even at Q2_K it needs about 17.5 GB; the Intel Core Ultra 200V (Arc 140V) 16GB has 9.1 GB usable.

What can the Intel Core Ultra 200V (Arc 140V) 16GB run instead?

Mellum2.1 Thinking (12B-A2.5B), Nemotron 3.5 Bolt E4B and K2 Horizon 7B run best on it; the page lists more, each with its speed.

What hardware can run Flint (40B-A2.5B)?

M2 Ultra 192GB, M1 Max 64GB and M4 Max 64GB 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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