Chandra Ocr 2 — Hardware Requirements & GPU Compatibility
VisionFunctions
Chandra OCR 2 is Datalab's 5.3-billion-parameter vision-language model for OCR and document conversion, turning scanned pages and PDFs into markdown, HTML, or JSON while preserving layout. It runs on a Qwen3.5-based backbone mixing linear attention with periodic full-attention layers, and handles handwriting, forms, tables, and math across 90-plus languages. It is the second generation of Datalab's Chandra model, improved over the first release. At just over 5 billion parameters, it runs on a single consumer GPU once quantized. Context extends to 262,144 tokens, generous for multi-page documents. It carries a modified OpenRAIL-M license: free for research, personal use, and startups under $2 million in funding or revenue, but it bars competing against Datalab's own API, more restrictive than a standard permissive license. Published in March 2026, following the original Chandra.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 128K)Best on 24 GB (Q4_K_M · ~180 tok/s · 256K)Macs from 8 GB (Q4_K_M · ~20 tok/s)
openrail
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Chandra Ocr 2
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/prithivMLmods/chandra-ocr-2-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 20.2 GB free · 16K context
Download: chandra-ocr-2.Q4_K_M.gguf3.07 GBprithivMLmods
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (8 GB headroom) · Q4_K_M
- Generation speed
- ~163 tok/s
- generation speed
- Cost per 1M output tokens
- $0.21
- per 1M output tokens
How Much VRAM Does Chandra Ocr 2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.7 GB | 3.2 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~241 tok/s | |
| 2.8 GB | 3.3 GB | 3-bit small — 3-bit small quantization | Fits: 256K context · ~236 tok/s | |
| 3.0 GB | 3.6 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~215 tok/s | |
| 3.1 GB | 3.6 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 256K context · ~210 tok/s | |
| 3.6 GB | 4.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~180 tok/s | |
| 4.2 GB | 4.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~155 tok/s | |
| 4.8 GB | 5.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~135 tok/s | |
| 5.8 GB | 6.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~114 tok/s |
Selected Q4_K_M
4-bit medium quantization3.8GBat 16K context
- Weights
- 3.2 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.2 GB free · ~180 tok/s
≥50 tok/s needs ≥280 GB/s · RTX 5090 ~319, RTX 3090 Ti ~180
Which Hardware Can Run Chandra Ocr 2?
Q4_K_M · 3.6 GBChandra Ocr 2 (Q4_K_M) requires 3.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 262K context window can add up to 5.2 GB, bringing total usage to 8.8 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Chandra Ocr 2, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.2 GB free · up to 128K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~135Simulate output on RTX 3080RTX 2080 Ti~110Simulate output on RTX 2080 TiRTX 3070 Ti~108Simulate output on RTX 3070 TiM3*8 GB~20Simulate output on iMac 24" M3 (8 GB)Orin Nano~18Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~13Simulate output on iPhone 1720 GB
fits · 16.2 GB free · up to 256K ctx
1 GPU
RX 7900 XT~132Simulate output on RX 7900 XT32–40 GB
fits · 28.2 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~319Simulate output on RTX 5090M5 Max36 GB~88Simulate output on Mac Studio M5 Max (36 GB)M532 GB~30Simulate output on MacBook Air 13" M5 (32 GB)Orin~37Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~24Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
4| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Gemma 4 E2BGoogle | — | 3.5 GB | ~191 | same size |
| Gemma 3n E2B ITGoogle | — | 5.9 GB | ~183 | same size |
| Qwen2.5 Omni 3BAlibaba | — | 6 GB | ~180 | same size |
| T5gemma 2B 2B Ul2Google | — | 6 GB | ~178 | same size |
Specifications
- Publisher
- datalab-to
- Parameters
- 5.3B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-16
- License
- openrail
Frequently Asked Questions
How much VRAM does Chandra Ocr 2 need?
Chandra Ocr 2 requires 3.6 GB of VRAM at Q4_K_M, or 11.1 GB at BF16. Full 262K context adds up to 5.2 GB (8.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 5.3B × 4.8 bits ÷ 8 = 3.2 GB
KV Cache + Overhead ≈ 0.4 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 ≈ 5.6 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for Chandra Ocr 2?
For Chandra Ocr 2, Q4_K_M (3.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (3.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.7 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Chandra Ocr 2 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Chandra Ocr 2. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 3.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run Chandra Ocr 2 locally?
Yes — Chandra Ocr 2 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Chandra Ocr 2?
At Q4_K_M, Chandra Ocr 2 can reach ~1315 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~180 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 ÷ 3.65 × 0.65 = ~1425 tok/s
Estimated speed at Q4_K_M (3.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Chandra Ocr 2?
At Q4_K_M, the download is about 3.18 GB. The full-precision BF16 version is 10.59 GB. The smallest option (Q2_K) is 2.25 GB.
Which GPUs can run Chandra Ocr 2?
52 consumer GPUs can run Chandra Ocr 2 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Chandra Ocr 2?
163 devices with unified memory can run Chandra Ocr 2 at Q4_K_M (3.6 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.