LocalFit

surya-ocr-2 Specialist

datalab-to/surya-ocr-2

ReleasedRelease date unknownSize0.7B tensors · denseRoleSpecialistLicenseopenrailContext256K config max

Where it runs 8K context · ≥ 5 t/s floor

SetupBest planMemoryGenerate
8 GB GPU + 32 GB RAMnative BF16vLLMFull GPUFits*≤ 202 t/s Ceiling
12 GB GPU + 32 GB RAMnative BF16vLLMFull GPUFits*≤ 253 t/s Ceiling
16 GB GPU + 64 GB RAMnative BF16vLLMFull GPUFits*≤ 630 t/s Ceiling
24 GB GPU + 64 GB RAMnative BF16vLLMFull GPUFits*≤ 709 t/s Ceiling
32 GB GPU + 128 GB RAMnative BF16vLLMFull GPUFits*≤ 1,260 t/s Ceiling
2× 24 GB GPUs + 128 GB RAMnative BF16vLLMFull GPUFits*≤ 658 t/s Ceiling
96 GB workstation GPU + 256 GB RAMnative BF16vLLMFull GPUFits*≤ 1,260 t/s Ceiling
Mac, 24 GBMLX 8-bitMLX-LMUnified memoryFits*~272–281 t/s Estimated
Mac, 48 GBMLX 8-bitMLX-LMUnified memoryFits*~272–281 t/s Estimated
Mac, 128 GBMLX 8-bitMLX-LMUnified memoryFits*~499–597 t/s Estimated
Mac, 512 GBMLX 8-bitMLX-LMUnified memoryFits*~593–855 t/s Estimated
DGX Spark 128 GBnative BF16vLLMUnified memoryFits*≤ 192 t/s Ceiling
Ryzen AI Max+ 395, 128 GBnative BF16vLLMUnified memoryFits*≤ 180 t/s Ceiling
CPU only, 64 GB DDR5-5600 dual channelDoes not fit

Same plan engine as the planner, with its default reserves and RAM. How plans are chosen.

Files exact Hugging Face bytes

ArtifactSizeFilesRevisionSource
MLX 8-bit 8-bit
Reza2kn/surya-ocr-2-mlx-8bit-g64
0.75 GiB1f528e13e6cHF files
MLX 4-bit 4-bit
Reza2kn/surya-ocr-2-mlx-4bit-g64
0.48 GiB1f0bd64cf7cHF files
Native safetensors BF16
datalab-to/surya-ocr-2
1.28 GiB13b3d4cdf88HF files

Not found in the representative quant search (not proven unavailable): GGUF FP16, GGUF Q8_0, GGUF Q5_K_M, GGUF Q4_K_M, EXL3, AWQ, GPTQ. Complete selected file sets; projectors, MTP/draft and lookup files are listed separately and budgeted only by plans that use them. File bytes are not allocated memory.

Quality

No Artificial Analysis evaluation mapped to this checkpoint.

Arena text rating

Unknown

Publisher-reported benchmarks

None extracted from the model card.

AA and Arena describe the hosted model in the stated mode, not each quantized file; scores are never inherited by base, pruned, abliterated or quantized variants and never averaged together.

How to run it

No model-specific recipe in the research. The planner binds the backend’s documented command template (llama.cpp, Ollama, LM Studio, MLX, vLLM) to your plan: open a plan’s details.

Recipes are source examples (settings are the publisher’s or author’s, not measured optima) and were not executed by LocalFit. A downloadable file does not prove your backend build supports the architecture.

Reported speeds 0

No reported measurement yet. Submit one.