Flint (40B-A2.5B) on Intel Core Ultra 200H (Arc 140T) (64 GB)

Can the Intel Core Ultra 200H (Arc 140T) (64 GB) run Flint (40B-A2.5B)?

Yes, it runs great

Runs great. At Q4_K_M it needs about 28.0 GB of 36.5 GB usable, with an estimated speed of 34.0 tok/s.

Best quant: Q4_K_M
Generation speed: 34.0 tok/s
Prompt processing: 2,024 tok/s
First token (1K prompt): 0.5 s
Largest safe context: 256K

Best app to run it on the Intel Core Ultra 200H (Arc 140T) (64 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 unsloth/flint-GGUF:UD-Q4_K_XL

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 unsloth/flint-GGUF:UD-Q4_K_XL

LM Studio Point-and-click: search, download and chat in one window.
Ollama Intel GPU support here is experimental (Vulkan).

Want an agent? Flint (40B-A2.5B) supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

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 Flint (40B-A2.5B)

Recommended for the Intel Core Ultra 200H (Arc 140T) (64 GB): Q4_K_M. 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 Q4_K_M (UD-Q4_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-Q4_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 85Excellent quality (A+) · 85 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 92/100 (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 great: How well it runs on the Intel Core Ultra 200H (Arc 140T) (64 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.	38.5 / 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.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	13.8 / 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 score	92.3 / 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 Core Ultra 200H (Arc 140T) (64 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	19.7 GB	36.5 GB	52.7 tok/s	Runs great	63Good quality (B) · 63 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q2_K_XL) (opens in a new tab)
`Q3_K_M`	23.3 GB	36.5 GB	42.8 tok/s	Runs great	75Great quality (A) · 75 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q3_K_XL) (opens in a new tab)
`Q4_K_M`	28.0 GB	36.5 GB	34.0 tok/s	Runs great	85Excellent quality (A+) · 85 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q4_K_XL) (opens in a new tab)
`Q5_K_M`	32.0 GB	36.5 GB	28.6 tok/s	Runs welltight fit	87Excellent quality (A+) · 87 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q5_K_XL) (opens in a new tab)
`Q6_K`	36.4 GB	36.5 GB	24.3 tok/s	Runs welltight fit	89Excellent quality (A+) · 89 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (UD-Q6_K_XL) (opens in a new tab)
`Q8_0`	45.9 GB	36.5 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`	83.3 GB	36.5 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.

Other hardware that runs it

90Excellent quality (A+) · 90 of 100: how capable the model is at F16, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking. Apple M2 Ultra (192 GB) F16 · 58.9 tok/s · Runs great
89Excellent quality (A+) · 89 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 Max (64 GB) Q6_K · 82.4 tok/s · Runs great
89Excellent quality (A+) · 89 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Max (64 GB) Q6_K · 102 tok/s · Runs great
85Excellent quality (A+) · 85 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Pro (48 GB) Q4_K_M · 83.1 tok/s · Runs great
85Excellent quality (A+) · 85 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q4_K_M · 222 tok/s · Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. AMD Ryzen AI Max+ 395 (128 GB) Q8_0 · 45.6 tok/s · Runs great

Quick answers

How much memory does Flint (40B-A2.5B) need on the Intel Core Ultra 200H (Arc 140T) 64GB?

At Q4_K_M it needs about 28.0 GB. The Intel Core Ultra 200H (Arc 140T) 64GB has 36.5 GB usable.

How fast is Flint (40B-A2.5B) on the Intel Core Ultra 200H (Arc 140T) 64GB?

About 34.0 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Flint (40B-A2.5B) should I use on the Intel Core Ultra 200H (Arc 140T) 64GB?

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.