DeepReinforce

Ornith 1.0 35B A3B

Name: Ornith 1.0 35B A3B
Rating: 81 (87 reviews)
Author: DeepReinforce

Frontera

HuggingFace

231.3KDescargas348Me gustaJun 2026Publicado262K tokensContextoMITLicencia72 FuerteCalidad

Ornith 1.0 35B A3B (35.099998474121094B parameters) requires approximately 23.2 GB of VRAM with Q4_K_M quantization. As a Mixture of Experts model with 3B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 27 GB of VRAM.

Comenzar

— copia y pega para ejecutar en local

Copy-paste commands to run Ornith 1.0 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "deepreinforce-ai/Ornith-1.0-35B" \
  --hf-file "Ornith-1.0-35B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Quick specs

Parameters35.1B (3B active)

Architecturemoe (MoE)

Context262K tokens

Modalitytext

Min RAM13.7 GB

Rec. RAM21.4 GB (Q4_K_M)

LicenseMIT

FamilyOrnith

✓ Code✓ Reasoning

About this model

Ornith-1.0-35B is DeepReinforce's lightweight self-improving coding agent, built on Qwen 3.5. A 35B-total MoE (256 experts, 8 active, ~3B activated per token) with a hybrid linear + full-attention backbone, designed for efficient single-GPU agentic deployment with a 262K context.

•Self-improving agentic RL: jointly refines solution generation and reasoning scaffolds.
•35B MoE with only ~3B activated per token — the lightweight member of the Ornith family.
•Hybrid linear-attention + full-attention (1-in-4 layers) for reduced KV-cache cost.
•262K context, tuned for single-GPU agentic coding.

Selecciones rápidas

Mejor económicoA

Mac mini M4 64GB~$1,099 — 13 tok/s

Mejor en generalS

RTX 5090 32GB~$1,999 — 139 tok/s

Mejor hardware

Mejores opciones para Ornith 1.0 35B A3B

RTX 5090 32GBS

32 GB

RTX PRO 4500 Blackwell 32GBS

32 GB

AMD Instinct MI100 32GBS

Ejecutar este modelo

Ornith 1.0 35B A3B on RTX 5090 32GB Ornith 1.0 35B A3B on RTX PRO 4500 Blackwell 32GB Ornith 1.0 35B A3B on AMD Instinct MI100 32GB

Opciones de cuantización

Estimaciones de VRAM por nivel de cuantización

No hardware detected — fit column shows raw VRAM estimates

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	—
Q3_K_S	3	17.2 GB	Low	—
NVFP4	4	19.7 GB	Medium	—
Q4_K_M	4	21.4 GB	Medium	—
Q5_K_M	5	25.3 GB	High	—
Q6_K	6	28.8 GB	High	—
Q8_0	8	37.6 GB	Very High	—
F16	16	72.0 GB	Maximum	—

Compatibilidad de hardware

Estimaciones de encaje en todo el hardware

Abrir calculadora

Computing compatibility...

Desglose de memoria

Reference: RTX 2060 6GB

Weights21.4 GB

KV Cache0.3 GB

Runtime0.9 GB

Headroom0.6 GB

Preguntas frecuentes

FAQ — Ornith 1.0 35B A3B

Ver también

Guía de cuantización Metodología de puntuación Abrir calculadora