Chandra — Hardware Requirements & GPU Compatibility
VisionFunctions
Chandra is Datalab's 8.8-billion-parameter vision-language OCR model, built on a Qwen3VL backbone, that converts document images and PDFs into markdown, HTML, or JSON while preserving layout, tables, math, and forms with checkboxes. It handles handwriting, multi-column and complex layouts, and extracts images and diagrams with captions and structured data across more than 40 languages, aiming at document digitization rather than general chat. On the olmOCR benchmark Chandra scored highest overall among compared models, ahead of Datalab's own Marker pipeline, Mistral's OCR API, DeepSeek-OCR, and anchored GPT-4o and Gemini Flash 2 baselines. At under 9 billion parameters it runs on a single consumer GPU. Context length is 262,144 tokens. It is released under an OpenRAIL license, a responsible-AI license that permits broad use but restricts certain harmful applications. It was published in October 2025 and has since been superseded by a newer Chandra OCR 2 model.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~112 tok/s · 64K)Macs from 16 GB (Q4_K_M · ~24 tok/s)
openrail
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Chandra
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/phate334/chandra-Q8_0-GGUFQ4_K_M fits fully on the RTX 4090 · 16.0 GB free · 16K context
Download: chandra-Q8_0-Q4_K_M.gguf5.26 GBphate334
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~101 tok/s
- generation speed
- Cost per 1M output tokens
- $0.34
- per 1M output tokens
How Much VRAM Does Chandra Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.3 GB | 8.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~151 tok/s | |
| 4.9 GB | 9.4 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~134 tok/s | |
| 5.0 GB | 9.5 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 64K context · ~131 tok/s | |
| 5.9 GB | 10.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~112 tok/s | |
| 6.8 GB | 11.4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 64K context · ~96 tok/s | |
| 7.8 GB | 12.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 64K context · ~84 tok/s | |
| 9.4 GB | 13.9 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 64K context · ~70 tok/s | |
| 18.1 GB | 22.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~36 tok/s |
Selected Q4_K_M
4-bit medium quantization8.0GBat 16K context
- Weights
- 5.3 GB
- KV cache
- 2.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.0 GB free · ~112 tok/s
≥50 tok/s needs ≥450 GB/s · RTX 5090 ~199, RTX 3090 Ti ~112
Which Hardware Can Run Chandra?
Q4_K_M · 5.9 GBChandra (Q4_K_M) requires 5.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 262K context window can add up to 38.4 GB, bringing total usage to 44.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Chandra, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 0.0 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~84Simulate output on RTX 3080RTX 2080 Ti~68Simulate output on RTX 2080 TiRTX 3070 Ti~68Simulate output on RTX 3070 TiOrin Nano~11Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.0 GB free · up to 64K ctx
1 GPU
RX 7900 XT~82Simulate output on RX 7900 XT32–40 GB
fits · 24.0 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~199Simulate output on RTX 5090M5 Max36 GB~55Simulate output on Mac Studio M5 Max (36 GB)M532 GB~18Simulate output on MacBook Air 13" M5 (32 GB)Orin~23Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~15Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| Ornith 1.0 9BDeep Reinforce | — | 6.5 GB | ~106 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
| MiniCPM O 4 5OpenBMB | — | 8.3 GB | ~105 | same size |
| Qwen3 VL 8B InstructAlibaba | — | 8 GB | ~112 | same size |
Specifications
- Publisher
- datalab-to
- Parameters
- 8.8B
- Architecture
- Qwen3VLForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-10-21
- License
- openrail
Frequently Asked Questions
How much VRAM does Chandra need?
Chandra requires 5.9 GB of VRAM at Q4_K_M, or 18.1 GB at BF16. Full 262K context adds up to 38.4 GB (44.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.8B × 4.8 bits ÷ 8 = 5.3 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 ≈ 38.9 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for Chandra?
For Chandra, Q4_K_M (5.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Chandra on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Chandra. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run Chandra locally?
Yes — Chandra can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Chandra?
At Q4_K_M, Chandra can reach ~819 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~112 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 ÷ 5.86 × 0.65 = ~887 tok/s
Estimated speed at Q4_K_M (5.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Chandra?
At Q4_K_M, the download is about 5.26 GB. The full-precision BF16 version is 17.53 GB. The smallest option (Q2_K) is 3.73 GB.
Which GPUs can run Chandra?
52 consumer GPUs can run Chandra at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Chandra?
151 devices with unified memory can run Chandra at Q4_K_M (5.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.