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Can the Intel Core Ultra 200V (Arc 140V) (32 GB) run DeepSeek Coder 33B Instruct?

30Basic quality (D) · 30 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly (tight fit)

Slow (tight fit). At Q2_K it needs about 17.5 GB of 18.2 GB usable, with an estimated speed of 6.3 tok/s.

Best quant
Q2_K
Generation speed
6.3 tok/s
Prompt processing
132 tok/s
First token (1K prompt)
7.8 s
Largest safe context
10K

Best app to run it on the Intel Core Ultra 200V (Arc 140V) (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 TheBloke/deepseek-coder-33B-instruct-GGUF:Q2_K
  • 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 TheBloke/deepseek-coder-33B-instruct-GGUF:Q2_K
  • LM Studio Point-and-click: search, download and chat in one window.
  • Ollama Intel GPU support here is experimental (Vulkan).

Want an agent? DeepSeek Coder 33B Instruct 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 DeepSeek Coder 33B Instruct

Recommended for the Intel Core Ultra 200V (Arc 140V) (32 GB): 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 deepseek-coder:33b-instruct-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/TheBloke/deepseek-coder-33B-instruct-GGUF (opens in a new tab)

ollama run hf.co/TheBloke/deepseek-coder-33B-instruct-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/deepseek-ai/deepseek-coder-33b-instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/deepseek-ai/DeepSeek-Coder (opens in a new tab)

git clone https://github.com/deepseek-ai/DeepSeek-Coder

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

How it’s rated

Quality 30Basic quality (D) · 30 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 58/100 (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 Slowtight fit
How well it runs on the Intel Core Ultra 200V (Arc 140V) (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.13.9 / 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.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.6.0 / 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 score58.8 / 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 200V (Arc 140V) (32 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 17.5 GB 18.2 GB 6.3 tok/s Slowtight fit 30Basic quality (D) · 30 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 20.5 GB 18.2 GB 4.2 tok/s Slowfar over GPU memory limit 36Basic quality (D) · 36 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 40/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 24.5 GB 18.2 GB — Won't run 41Fair quality (C) · 41 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 27.8 GB 18.2 GB — Won't run 42Fair quality (C) · 42 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 31.5 GB 18.2 GB — Won't run 43Fair quality (C) · 43 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 39.5 GB 18.2 GB — Won't run 44Fair quality (C) · 44 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 70.8 GB 18.2 GB — Won't run 44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. 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

Quick answers

How much memory does DeepSeek Coder 33B Instruct need on the Intel Core Ultra 200V (Arc 140V) 32GB?

At Q2_K it needs about 17.5 GB. The Intel Core Ultra 200V (Arc 140V) 32GB has 18.2 GB usable.

How fast is DeepSeek Coder 33B Instruct on the Intel Core Ultra 200V (Arc 140V) 32GB?

About 6.3 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of DeepSeek Coder 33B Instruct should I use on the Intel Core Ultra 200V (Arc 140V) 32GB?

Q2_K. 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.

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