MicrosoftQwen3VLForConditionalGeneration

AesCode 32B — Hardware Requirements & GPU Compatibility

VisionCodeFunctions

AesCode-32B is a Microsoft research model that writes slides, posters, dashboards, and infographics as editable HTML and CSS. It starts from Qwen3-VL-32B-Instruct and is trained with supervised fine-tuning followed by reinforcement learning on several reward signals. It can take an image generated from the same prompt as a style reference, which it uses for layout and look while taking the content from the text prompt. On the paper's 300-sample infographic test, it scored 85.89 overall, ahead of GPT-5.5 and Claude Opus 4.8 given the same reference. At about 33 billion parameters, a Q4 quant fits a 24 GB GPU. The context window is 262,144 tokens. It is released under the Apache 2.0 license. Because it is a vision-language model, it reads images as well as text. It is a specialist for design-to-code output rather than a general chat model, and the card pitches it for generating editable visual documents and for research on multimodal code generation. A smaller AesCode-8B sibling is also available.

33.4B params · dense262K contextApache 2.0Sep 2026611 downloads52 likes10.3K quant downloads

Based on Qwen3 VL 32B Instruct

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

Detection reads your browser's GPU name with no data sent anywhere.

B

Decent on a 24 GB GPU

Runs from 16 GB (IQ3_XXS · 16K)Best on 24 GB (Q4_K_M · ~32 tok/s · 16K)Macs from 32 GB (~4 tok/s)

Apache 2.0

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run AesCode 32B

One recommended way. Every other method, OS and quant is in the step-by-step guide.

Recommended

Ollama

one command · needs Ollama installed

$ollama run hf.co/bartowski/AesCode-32B-GGUF:Q4_K_M

Q4_K_M fits fully on the RTX 4090 · 1.0 GB free · 16K context

Download: AesCode-32B-Q4_K_M.gguf20.49 GBbartowski

install, OS, other quants

Other ways

each opens step-by-step

Run in cloud

Fits on RTX A6000 (48 GB) (25 GB headroom) · Q4_K_M

Generation speed: ~24 tok/s; generation speed
Cost per 1M output tokens: $3.79; per 1M output tokens

Deployon RunPodBest optionfrom $0.33/hr

Compare GPUs →

or

How Much VRAM Does AesCode 32B Need?

Select a quantization level; the listed hardware adheres to it.

Quantization	VRAM	+32K	+128K	File	Quality	On RTX 4090 (reference)
	9.8 GB	14.8 GB	31 GB	10.6 GB	2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss	Fits: 64K context · ~67 tok/s
	10.6 GB	15.7 GB	31.8 GB	11.4 GB	2-bit · i-quant extra small — Importance-weighted 2-bit, extra small	Fits: 64K context · ~62 tok/s
	11.1 GB	16.1 GB	32.2 GB	12 GB	2-bit · i-quant small — Importance-weighted 2-bit, small	Fits: 64K context · ~59 tok/s
	11.9 GB	16.9 GB	33 GB	12.7 GB	2-bit · i-quant medium — Importance-weighted 2-bit, medium	Fits: 64K context · ~55 tok/s
	13.6 GB	18.6 GB	34.7 GB	13.6 GB	3-bit · i-quant — Importance-weighted 3-bit	Fits: 64K context · ~48 tok/s
	14.0 GB	19 GB	35.1 GB	11.5 GB	2-bit · small K-quant — 2-bit small K-quant	Fits: 32K context · ~47 tok/s
	14.4 GB	19.4 GB	35.5 GB	14.4 GB	3-bit · i-quant extra small — Importance-weighted 3-bit, extra small	Fits: 32K context · ~46 tok/s
	14.8 GB	19.8 GB	36 GB	14.4 GB	3-bit · i-quant small — Importance-weighted 3-bit, small	Fits: 32K context · ~44 tok/s
	14.8 GB	19.8 GB	36 GB	13 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Fits: 32K context · ~44 tok/s
	15.2 GB	20.3 GB	36.4 GB	14.9 GB	3-bit small — 3-bit small quantization	Fits: 32K context · ~43 tok/s
	15.7 GB	20.7 GB	36.8 GB	15.5 GB	3-bit · i-quant medium — Importance-weighted 3-bit, medium	Fits: 32K context · ~42 tok/s
	16.9 GB	21.9 GB	38 GB	16.5 GB	3-bit medium — 3-bit medium quantization	Fits: 32K context · ~39 tok/s
	17.3 GB	22.3 GB	38.5 GB	19 GB	4-bit · legacy — 4-bit legacy quantization	Fits: 32K context · ~38 tok/s
	17.7 GB	22.8 GB	38.9 GB	17.5 GB	3-bit large — 3-bit large quantization	Fits: 32K context · ~37 tok/s
	18.6 GB	23.6 GB	39.7 GB	18.1 GB	4-bit · i-quant compact — Importance-weighted 4-bit, compact	Fits: 32K context · ~35 tok/s
	19.4 GB	24.4 GB	40.5 GB	19.1 GB	4-bit small — 4-bit small quantization	Fits: 16K context · ~34 tok/s
	19.4 GB	24.4 GB	40.5 GB	19.1 GB	4-bit · i-quant non-linear — Importance-weighted 4-bit, non-linear	Fits: 16K context · ~34 tok/s
	19.4 GB	24.4 GB	40.5 GB	20.9 GB	4-bit · legacy+ — 4-bit legacy quantization with offset	Fits: 16K context · ~34 tok/s
	20.6 GB	25.7 GB	41.8 GB	20.5 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Fits: 16K context · ~32 tok/s
	21.1 GB	26.1 GB	42.2 GB	21.1 GB	4-bit large — 4-bit large quantization	Fits: 16K context · ~31 tok/s
	23.6 GB	28.6 GB	44.7 GB	22.8 GB	5-bit small — 5-bit small quantization	Fits: 4K context · ~28 tok/s
	24.4 GB	29.4 GB	45.5 GB	23.8 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Does not fit: needs 26.8 GB
	28.2 GB	33.2 GB	49.3 GB	28 GB	6-bit, very good — 6-bit quantization, very good quality	Does not fit: needs 30.5 GB
	34.0 GB	39 GB	55.1 GB	34.8 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Does not fit: needs 36.3 GB
	67.3 GB	72.4 GB	88.5 GB	65.5 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Does not fit: needs 69.7 GB

Selected Q4_K_M

4-bit medium quantization

23.0GBat 16K context

Weights: 20.0 GB
KV cache: 2.7 GB
Overhead: 0.3 GB

Fits RTX 4090 (reference) · 1.0 GB free · ~32 tok/s

≥50 tok/s needs ≥1.59 TB/s · RTX 5090 ~56

Which Hardware Can Run AesCode 32B?

Q4_K_M · 20.6 GB

AesCode 32B (Q4_K_M) requires 20.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 262K context window can add up to 42.6 GB, bringing total usage to 63.3 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 103 devices with unified memory can run AesCode 32B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M1 Max (32 GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M

Typical hardwareQuantVRAMtok/sContext that fits

8–12 GB · RTX 4060, RTX 3060 12GBDoesn't fit — smallest quant needs 12.2 GB8 GB tier →12 GB tier →

16 GB · RTX 5060 Ti 16GBIQ3_XXS · 15.9 GB · ~22 tok/s · 16K15.9~2216K16 GB tier →

24 GB · RTX 4090referenceQ4_K_M · 23 GB · ~32 tok/s · 16K23~3216K24 GB tier →

Mac 32 GB · Mac Mini M4Q4_K_M · 23 GB · ~4 tok/s · 32K23~432KMacs →

Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 23 GB · ~19 tok/s · 128K23~19128KMacs →

Alternatives to AesCode 32B

Similar models

6tok/s on RTX 4090

Model	Score	VRAM	tok/s	Why it is similar
Qwen3.6 35B A3BAlibaba	107.1	22 GB	~151	same size
MiniCPM V 4.7 35B A3BOpenBMB	—	22.1 GB	~169	same size
Ornith 1.5 35B A3Bornith-ai	—	22 GB	~151	same size
NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA	—	19.3 GB	~197	same size
Agents A1InternScience	—	21.5 GB	~171	same size
EXAONE 4.5 33BLGAI-EXAONE	—	23.9 GB	~33	same size

Specifications

Publisher: Microsoft
Parameters: 33.4B
Architecture: Qwen3VLForConditionalGeneration
Context Length: 262,144 tokens
Vocabulary Size: 151,936
Release Date: 2026-09-29
License: Apache 2.0

Resources

Hugging Face model card (opens in a new tab)

Frequently Asked Questions

How much VRAM does AesCode 32B need?

AesCode 32B requires 20.6 GB of VRAM at Q4_K_M, or 67.3 GB at BF16. Full 262K context adds up to 42.6 GB (63.3 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 33.4B × 4.8 bits ÷ 8 = 20 GB

KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)

Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.

KV Cache + Overhead ≈ 43.3 GB (at full 262K context)

VRAM usage by quantization

Q4_K_M

20.6 GB

Q4_K_M + full context

63.3 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run AesCode 32B?

Yes, at Q5_K_S (23.6 GB) or lower. Higher quantizations like Q5_K_M (24.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for AesCode 32B?

For AesCode 32B, Q4_K_M (20.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (21.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.8 GB.

VRAM requirement by quantization

IQ2_XXS

9.8 GB~53%

IQ3_XS

14.4 GB~73%

Q4_0

17.3 GB~85%

Q4_K_M ★

20.6 GB~89%

Q4_K_L

21.1 GB~90%

BF16

67.3 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run AesCode 32B on a Mac?

Yes, but only at lower quantizations. The smallest Mac that can run AesCode 32B is iPad Pro M5 13" (16 GB) at IQ2_XXS; 136 of the 156 Macs we list can run it at some quantization. For Q4_K_M (20.6 GB) you need a Mac with more unified memory.

Can I run AesCode 32B locally?

Yes — AesCode 32B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is AesCode 32B?

At Q4_K_M, AesCode 32B can reach ~232 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: NVIDIA B200 → 8000 ÷ 20.6 × 0.65 = ~252 tok/s

Estimated speed at Q4_K_M (20.6 GB)

NVIDIA B200

~252 tok/s

NVIDIA GeForce RTX 4090

~32 tok/s

NVIDIA B300

~252 tok/s

AMD Instinct MI350X

~232 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of AesCode 32B?

At Q4_K_M, the download is about 20.01 GB. The full-precision BF16 version is 66.71 GB. The smallest option (IQ2_XXS) is 9.17 GB.

Which GPUs can run AesCode 32B?

7 consumer GPUs can run AesCode 32B at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.

Which devices can run AesCode 32B?

103 devices with unified memory can run AesCode 32B at Q4_K_M (20.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.

Run AesCode 32B

23.0 GB VRAM at 16K context · fits the RTX 4090 (1.0 GB free)

Works on macOS, Windows and Linux.

Install Ollama (Linux)
$curl -fsSL https://ollama.com/install.sh | sh
Install Ollama (macOS / Windows)
Download and run the app; it installs the ollama command.
Download Ollama ↗
Run Q4_K_M
$ollama run hf.co/bartowski/AesCode-32B-GGUF:Q4_K_M
Raise the context window
Type it inside the chat. For the API server instead, start it with OLLAMA_CONTEXT_LENGTH=16384 ollama serve.
$/set parameter num_ctx 16384
A larger context needs more memory than the weights alone.
Use the API
Ollama serves its API on http://localhost:11434.

Works on macOS, Windows and Linux.

Install LM Studio
Desktop app for macOS (Apple silicon), Windows and Linux.
lmstudio.ai ↗
Download the model
In the app press Ctrl/Cmd + Shift + M and paste https://huggingface.co/bartowski/AesCode-32B-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/bartowski/AesCode-32B-GGUF@Q4_K_M
Open on Hugging Face ↗
Load and chat
Open the model loader, select the model and set Context Length to 16384. Leave GPU offload at maximum if it fits.
Start the local server (optional)
$lms server start

Works on macOS, Windows and Linux.

Install llama.cpp (macOS)
MacPorts, Nix and conda-forge also work.
$brew install llama.cpp
Install options ↗
Install llama.cpp (Windows)
$winget install llama.cpp
Install llama.cpp (Linux)
Download the prebuilt llama-*-bin-ubuntu-* archive for your GPU backend (CPU, Vulkan) from the releases page, unpack it and run the binaries from that folder. Homebrew (brew install llama.cpp) and conda-forge also work on Linux.
llama.cpp releases ↗
Start the server
Downloads the model on first run. The chat UI and an OpenAI-compatible API are at http://localhost:8080.
$llama-server -hf bartowski/AesCode-32B-GGUF:Q4_K_M -c 16384 -ngl 99 --port 8080

Works on macOS, Windows and Linux.

Option A: Docker Model Runner
Docker Desktop: Settings > AI > Enable Docker Model Runner. Docker Engine on Linux: install the docker-model-plugin package.
$docker model run hf.co/bartowski/AesCode-32B-GGUF:Q4_K_M
Model Runner docs ↗
The quant tag after the repo is how Hugging Face cards write it; if your Docker version rejects it, drop the tag and pick the quant by repo.
Option B: llama.cpp server image (NVIDIA)
Needs the NVIDIA Container Toolkit. Models are cached in the llama-cache volume. Open http://localhost:8080.
$docker run --gpus all -p 8080:8080 -v llama-cache:/root/.cache ghcr.io/ggml-org/llama.cpp:server-cuda -hf bartowski/AesCode-32B-GGUF:Q4_K_M -c 16384 -ngl 99 --host 0.0.0.0 --port 8080
Image tags (cuda13, vulkan, rocm) ↗
Cache path inside the image is not in the docs; remove the -v flag if the volume complains.

Linux only.

Install vLLM
Linux only; on Windows run it inside WSL2. NVIDIA GPU shown; AMD ROCm, Intel and CPU builds have their own install pages.
$uv venv --python 3.12 --seed
$source .venv/bin/activate
$uv pip install vllm --torch-backend=auto
vLLM installation ↗
Serve the model
OpenAI-compatible API at http://localhost:8000/v1.
$vllm serve microsoft/AesCode-32B --max-model-len 16384
Runs the original ~73 GB weights, not the Q4_K_M GGUF.

Works on macOS, Windows and Linux.

Install Jan
jan.ai ↗
Find the model
Open the Hub tab, paste this model ID in Search, and choose Q4_K_M if offered.
$bartowski/AesCode-32B-GGUF
Use the local API (optional)
Settings > Local API Server > Start Server. It listens on http://127.0.0.1:1337.

Rent GPUs hourly to run Q4_K_M right there. Pricing comes from partner marketplaces and varies.

Available GPU rentals for Q4_K_M, prioritized by value
GPU	Speed	$/1M tokens	Rent
RTX A6000 (48 GB)CheapestCheapest25 GB free	~24 tok/s	$3.79	Deploy on RunPodfrom $0.33/hr (opens in new tab)	48 GB · 25 GB free · ~24 tok/s · $3.79 / 1M tokens
RTX PRO 4500 (32 GB)9 GB free	~28 tok/s	$3.35	Deploy on RunPodfrom $0.34/hr (opens in new tab)	32 GB · 9 GB free · ~28 tok/s · $3.35 / 1M tokens
A40 (48 GB)25 GB free	~22 tok/s	$4.44	Deploy on RunPodfrom $0.35/hr (opens in new tab)	48 GB · 25 GB free · ~22 tok/s · $4.44 / 1M tokens
RTX A6000 (48 GB)25 GB free	~24 tok/s	$4.63	Deploy on Vast.aifrom $0.40/hr (opens in new tab)	48 GB · 25 GB free · ~24 tok/s · $4.63 / 1M tokens
RTX 5000 Ada (32 GB)9 GB free	~18 tok/s	$7.52	Deploy on RunPodfrom $0.49/hr (opens in new tab)	32 GB · 9 GB free · ~18 tok/s · $7.52 / 1M tokens
RTX 5090 (32 GB)9 GB free	~56 tok/s	$2.90	Deploy on Vast.aifrom $0.59/hr (opens in new tab)	32 GB · 9 GB free · ~56 tok/s · $2.90 / 1M tokens
RTX 6000 Ada (48 GB)25 GB free	~30 tok/s	$5.70	Deploy on Vast.aifrom $0.62/hr (opens in new tab)	48 GB · 25 GB free · ~30 tok/s · $5.70 / 1M tokens
A100 40GB17 GB free	~49 tok/s	$3.99	Deploy on Vast.aifrom $0.70/hr (opens in new tab)	17 GB free · ~49 tok/s · $3.99 / 1M tokens

Compare GPUs →