Deploy any modelin 2 lines of code
The fastest way to deploy and fine-tune open-source models at production scale.
# Deploy any model in 2 lines
import jobim
client = jobim.Client(api_key="jbm_123")
response = client.infer("deepseek-r1", "Explain quantum computing")The J-Factor Magic
See how our proprietary compression transforms massive, expensive models into lean, cost-efficient inference engines
~ $0.005 / Token
DENSE & HIGH LATENCY
J-FACTOR ENGINE
Proprietary 98.2% Compression
**$0.0001 / Token**
ULTRA-FAST & 13.6 TPS
client.infer("jobim-jfactor-utility", "Your prompt here...")
You request the optimized model; J-Factor handles all compression and infrastructure.
The Jobim Inference Flow
From zero to production-ready AI in under 60 seconds.
Acquire
Fast Track Access
Instantly provision your inference environment. Get $10 in free credits and your unique API key.
Optimize & Prepare
The J-Factor
Select from our catalog of 98.2% compressed models. J-Factor instantly optimizes for extreme low-latency.
client.infer("llama-70b", prompt)Deploy & Scale
Instant Production
Call the API and stream results. We handle auto-scaling, zero cold starts, and 99.99% uptime.
DeepSeek-R1
DeepSeek
State-of-the-art reasoning model for math, code, and logic tasks with 671B parameters.
Llama 3.3 70B
Meta
High-performance instruction-tuned model optimized for conversational AI.
DeepSeek-Coder
DeepSeek
Best-in-class coding model with 33B parameters and superior code generation.
Qwen2.5 72B
Qwen
Powerful 72B parameter model excelling in reasoning and multilingual tasks.
CRAD 7B
Jobim AI
Our flagship compressed model with 98.2% size reduction and 13.6 TPS throughput.
Mistral 8x22B
Mistral AI
MoE model with 176B total parameters delivering exceptional quality and efficiency.
Llama 3.2 11B
Meta
Vision-language model with strong multimodal understanding capabilities.
Gemma 2 27B
Efficient 27B parameter model optimized for edge deployment and fast inference.
Phi-3 Medium
Microsoft
14B parameter model delivering large-model capabilities in compact size.
Fine-tune modelswith one API call
Full fine-tuning, LoRA, and DPO support with automatic optimization
LoRA Fine-tuning
Memory-efficient fine-tuning that produces small adapters for inference
import jobim
# Start LoRA fine-tuning
jobim.fine_tuning.create(
"deepseek-ai/DeepSeek-R1", # Base model
"training-file.jsonl", # Your dataset
method="lora", # LoRA training
target_modules="all-linear" # Optimize all linear layers
)DPO Training
Align models with human preferences using Direct Preference Optimization
import jobim
# Start DPO training
jobim.fine_tuning.create(
"meta-llama/Llama-3.3-70B",
"preference-dataset.jsonl",
method="dpo", # DPO training
dpo_beta=0.1, # DPO beta parameter
learning_rate=1e-6
)Built fordevelopers
No cold starts
Instant inference with always-warm containers and 13.6 TPS throughput
Pay-per-token
$0.0001 per token with 98.2% compression. No hidden fees or GPU costs
Simple API
Deploy any model in 2 lines of code with full TypeScript and Python SDKs
Start building in seconds
Deploy your first model with $10 free credits. No credit card required.