Skip to content
Only Tutorials
July 23, 2025Wednesday
  1. Modal Blog52

    Modal benchmarks open ASR models for batch transcription at 100x speed and cost gains

    Modal implemented a batch transcription service on its platform using NVIDIA's parakeet-tdt-0.6b-v2 and canary-1b-flash, reporting over 100x faster or 100x cheaper end-to-end performance than a popular proprietary ASR API while matching its error rate on roughly a week of ESB benchmark audio. The team attributes the gains to shuffling files across request batches, sorting audio by duration within GPU batches, parallel downloads, and empirically chosen GPU and worker configurations. The post walks through the architecture and optimization steps so others can build similar services.

July 2, 2025Wednesday
  1. Modal Blog40

    How Modal Used Evals and Inference-Time Compute Scaling to Generate Scannable QArt Codes

    Modal used evals and inference-time compute scaling to improve its QArt code system, boosting QR scan rate to its 95% service-level objective while improving aesthetic quality and returning codes in under 20s at p95. The work follows the standard ML playbook of collecting human judgments, aligning to them, then scaling, addressing the "it worked on my prompt" gap that leaves many generated QR codes unscannable.

June 30, 2025Monday
  1. Modal Blog28

    How Quora uses Modal to run thousands of Python sandboxes simultaneously

    Quora uses Modal Sandboxes to securely execute LLM-generated code in Poe, its AI chatbot platform, shipping the feature months earlier than an in-house build would have allowed. The team stress-tested Sandbox creation throughput to 1000 sandboxes per second and reports saving 2 engineers' worth of ongoing infrastructure maintenance time.

June 24, 2025Tuesday
  1. Vapi Blog22

    Mistral vs Llama 3: A Complete Comparison for Voice AI Applications

    Mistral and Llama 3 split on philosophy for voice AI: Mistral's 7B to Mistral Small 3.1 (24B) models favor low-latency efficiency, while Llama 3 scales from 8B to 70B for stronger reasoning and multilingual reach. Mistral Small 3.1 reaches 0.29s time to first token at 150-166 tokens per second, and Mistral Small 24B beats Llama 3.1 8B Instruct on ARC-C, GPQA and MMLU, while Llama 3.1 405B scores 85.2% on MMLU and 96.8% on GSM8K.

June 23, 2025Monday
  1. Vapi Blog22

    8 Alternatives to Azure for Voice AI STT on Vapi

    Vapi now offers eight speech-to-text providers — Gladia, AssemblyAI, Deepgram, Cartesia, Talkscriber, OpenAI, Speechmatics and ElevenLabs — as drop-in alternatives to Azure for voice agents. Switching providers takes a single JSON parameter change, letting developers benchmark latency, cost and compliance. Gladia claims sub-100 ms end-to-end latency, AssemblyAI bundles summaries and PII redaction in one API call, and Cartesia runs fully on-premises.

June 19, 2025Thursday
  1. Vapi Blog22

    Gemini Flash vs Pro: Comparing Google's Latest LLMs

    Google's Gemini 2.5 Flash and 2.5 Pro differ mainly in speed versus reasoning depth, both offering 1M-token context windows and native availability in Vapi for voice agents. Flash returns its first token in 0.21–0.37 seconds at 163 tokens per second and costs $0.15/$0.60 per million input/output tokens, while Pro costs $1.25/$10 and adds a Deep Think mode, with a 2M-token window planned.

June 18, 2025Wednesday
  1. Modal Blog58

    Modal shows how to run FLUX.1-dev three times faster

    Modal engineers describe making Black Forest Labs' FLUX.1-dev run about three times faster as an autoscaling service, cutting a 1024x1024 image from roughly 6.75 seconds to under three. Standard optimizations, namely the Torch compiler with max-autotune, fused QKV attention and channels-last memory layout, gave about 1.5x, while First Block Caching from the ParaAttention repo, based on the TEACache paper, added another 2x at a threshold of 0.12. Compile caches and Modal Memory Snapshots cut cold start latency by 30x.

  2. Vapi Blog18

    Five Ways Real Estate Agents Can Use AI

    A guide outlines five ways real estate agents can use AI, including voice agents built on Vapi that answer calls, qualify leads, and book showings around the clock. It also covers automating content with ChatGPT and Canva, plus AI pricing tools such as HouseCanary and CoreLogic that produce valuations with confidence scores.

June 17, 2025Tuesday
  1. Vapi Blog38

    Deepgram Nova-3 vs Nova-2: How the STT Engine Evolved

    Deepgram's Nova-3 cuts streaming word error rate from Nova-2's 8.4% to 6.84% and reaches 5.26% batch WER, a 54.3% reduction in streaming errors. Nova-3 replaces Nova-2's separate domain-specific models with a single adaptable network and adds real-time multilingual conversation handling, up to 100 self-serve custom terms without retraining, and redaction for up to 50 entities. Both models run natively on Vapi with identical endpoints, so switching is a parameter change, though Nova-3 sits at Deepgram's premium pricing tier.

June 4, 2025Wednesday
  2. Vapi Blog22

    Three Examples of Voice AI Improving Logistics

    Vapi outlines three voice AI use cases in logistics: ShipEX uses voice intelligence across 350 trucks to route maintenance alerts and accident reports, the Idaho State Liquor Division's voice picking system lifted pick rates 35%, and maintenance technicians log issues by voice to auto-create CMMS work orders. The article also details API integration with TMS, CRM, and WMS systems and deployment considerations.

May 29, 2025Thursday
  1. Vapi Blog25

    Choosing Between Gemini Models for Voice AI

    Vapi outlines how to pick among four Gemini models for voice agents: Gemini 1.0 Pro for predictable, compliance-friendly flows at $0.50 input and $2.00 output per million tokens, Gemini 1.5 Flash for high-volume work with a 1M token context window, Gemini 1.5 Pro for deep reasoning with a 2M token window and 99.7% long-context retrieval accuracy, and Gemini 2.0 Flash for native tool calling.

May 28, 2025Wednesday
May 26, 2025Monday
  1. Vapi Blog10

    Introduction to SRTP: Secure Real-Time Transport Protocol

    SRTP secures RTP voice and video streams by adding encrypted payloads, authentication tags, and a master key identifier to each media packet. It relies primarily on AES counter mode and HMAC-SHA1 authentication tags, with sequence numbers blocking replay attacks, and pairs with DTLS-SRTP or SDES for key exchange. SRTP is positioned as a lower-overhead, application-layer option for real-time communications, with GDPR, HIPAA, and CPRA cited as compliance drivers.

  2. Vapi Blog18

    Env Files and Environment Variables for Voice AI Projects

    A guide on using .env files and environment configuration in voice AI projects, covering dotenv setup for Node.js and Python, adding .env to .gitignore, and Vapi-specific keys like VAPI_API_KEY and VAPI_PROJECT_ID. It cites GitGuardian's finding of 12.8 million secrets leaked on GitHub in 2023, a 28% increase, and recommends separate env files per environment plus tools such as git-crypt, SOPS, AWS Secrets Manager and HashiCorp Vault.

May 23, 2025Friday
  1. Vapi Blog18

    Amazon Lex vs Dialogflow: Complete Platform Comparison Guide

    Amazon Lex suits teams already on AWS needing tight integration with Amazon services, while Dialogflow offers broader language support and an easier learning curve for newcomers. Lex uses Alexa's technology with pay-as-you-go pricing including a free tier of 10,000 text and 5,000 speech requests monthly for the first year, and Dialogflow supports over 30 languages with pre-built agents and sentiment analysis.

  2. Vapi Blog8

    Mastering Environment Variables for Vapi Voice AI Integration

    Vapi's guide explains how to configure environment variables for its Voice AI integration, covering temporary vs. permanent and user-level vs. system-level variables. It details setup on Windows, macOS and Linux with Vapi-specific examples like VAPI_API_KEY and VAPI_MULTILINGUAL_SUPPORT, plus naming and security best practices such as never hardcoding API keys.

May 22, 2025Thursday
  1. Vapi Blog20

    The 10 Best Open-Source Medical Speech-to-Text Tools

    A guide ranks 10 open-source speech-to-text models for clinical use, evaluated on real-time accuracy, clinical terminology handling, operational reliability and data security. The list covers Vosk, DeepSpeech, SpeechBrain, OpenAI Whisper and ESPnet, matched to settings such as low-resource rural clinics, multilingual hospitals and terminology-heavy research centers. All can be integrated with Vapi's model-agnostic voice AI platform.

May 7, 2025Wednesday
April 18, 2025Friday
  1. Modal Blog22

    How sync. uses Modal to lipsync 100 hours of video a day

    sync. is using Modal to lipsync over 100 hours of video a day, splitting long videos into scenes that run face detection and translation across n T4 GPU containers before passing each scene to its proprietary LipSync GenAI model on A100s. The team deploys up to 95 times a day and shipped 10 major model variants to production in one year, after moving off a Google Colab prototype and AWS Lambda.

April 1, 2025Tuesday
  1. Vapi Blog20

    Scaling Client Intake with Vapi Voice AI Agents: A Consultant's Case Study

    Consultant Christian Ulstrup of GSD at Work used Vapi voice AI agents to replace scheduled discovery calls with an asynchronous voice intake system for his AI Action Sessions workshops. Participants call a dedicated Vapi phone line at their convenience, and transcripts plus AI-generated summaries flow into Slack via Zapier and are logged in HubSpot. For one recent client, 20 participants across the globe contributed over 3 hours of discovery, and the session earned a 5 out of 5 rating.

February 24, 2025Monday
  1. Modal Blog40

    A high-level guide to GPU utilization: allocation, kernel, and Model FLOP/s utilization

    Modal published a guide distinguishing three things commonly called "GPU utilization": GPU Allocation Utilization (GPU-seconds running application code over GPU-seconds paid for), GPU Kernel Utilization (fraction of time running code on GPUs), and Model FLOP/s Utilization (share of theoretical arithmetic bandwidth used). The guide focuses on neural network inference workloads, which Modal calls a revenue center rather than a cost center.

December 10, 2024Tuesday
  1. Modal Blog30

    What is LLM fine-tuning? A Guide to Techniques and Tools

    Fine-tuning adjusts pretrained LLM weights to fit a specific task or domain, delivering higher quality than prompt engineering at lower cost and latency; a fine-tuned Llama 7B can be roughly 50 times more cost-effective per token than GPT-3.5 with comparable performance. The guide covers use cases such as structured output, style adherence and domain-specific instruction, plus options including OpenAI, Predibase, Modal, Google Colab and AWS SageMaker, and frameworks HuggingFace transformers, TRL and Axolotl.

November 20, 2024Wednesday
September 24, 2024Tuesday
September 18, 2024Wednesday
August 5, 2024Monday
  1. Modal Blog62

    Modal replicates Large Language Monkeys: LLaMA 3.1 8B matches GPT-4o on HumanEval via search

    Modal's Charles Frye replicated the core claim of the Large Language Monkeys paper on a new dataset, showing that repeated sampling lifts LLaMA 3.1 8B from 66.4% pass@1 to 90.5% pass@100 and 95.1% pass@1000 on HumanEval, versus GPT-4o's reported 90.2% pass@1. The runs used vLLM on Modal serverless GPUs and Modal Sandboxes for parallel test execution, cost well under $50, and showed smooth, predictable scaling across three orders of magnitude.

    Why it matters: A hands-on replication shows how repeated sampling with a small open model can approach GPT-4o on HumanEval, offering a concrete, low-cost recipe for trading compute for accuracy.

June 20, 2024Thursday
  1. Modal Blog25

    Run GPU jobs from Airflow with Modal

    Modal can now be triggered directly from an Airflow DAG, letting teams run GPU-backed AI/ML tasks without setting up Celery or Kubernetes executors. The guide covers installing Modal in an Airflow environment, setting MODAL_TOKEN_ID and MODAL_TOKEN_SECRET, and two approaches: calling deployed Modal Functions via lookup, or building a custom operator that runs Python in Modal Sandboxes. A Modal Airflow provider package is in progress.

June 6, 2024Thursday
  1. Modal Blog38

    How Modal catches crypto miners using syscall signatures

    Modal added a runtime program-analysis component called seccheck to its serverless container runtime, detecting and disabling cryptomining Functions before banning the offending users. Because miners can change metadata but not their programs, Modal analyzes syscall logs rather than source code, binaries, or container images. The system avoids ptrace-based strace overhead, which can slow syscall-heavy programs by over 100x.

May 23, 2024Thursday
  1. Modal Blog22

    How Hunch Uses Modal Sandboxes to Run AI-Generated Code Safely

    Hunch, a spatial canvas that combines models such as OpenAI GPT-4o, Anthropic Claude 3 Opus and Stable Diffusion 3, uses Modal Sandboxes to execute AI-generated code safely in isolated, ephemeral containers. The integration required only a few lines of Python and supports pip installs, attached storage volumes and automatic scaling. Hunch said the setup lets non-coders build workflows that fetch API data or send results to Slack webhooks.

May 21, 2024Tuesday
  1. Modal Blog31

    Fine-tuning Stable Diffusion on Heroicons to Create an Infinite Icon Library

    Modal fine-tuned a Stable Diffusion model on the roughly 300-SVG Heroicons library to generate new black-and-white line icons in that style, including ones for concepts like barack-obama and golden-retriever. The full pipeline — downloading the pretrained model, full fine-tuning with HuggingFace Diffusers' train_text_to_image.py script, and serving a Gradio web UI — runs on Modal's serverless GPU platform. The write-up walks through dataset prep (SVG-to-PNG conversion, white backgrounds, caption metadata.csv) and notes that full fine-tuning worked best, though LoRA-style parallel adapters can also work for small datasets.

April 26, 2024Friday
  1. Modal Blog50

    Fine-tuning an open source embedding model to beat OpenAI's text-embedding-3-small

    A fine-tuned open source embedding model can outperform OpenAI's text-embedding-3-small in accuracy at lower cost using only a handful of examples. Modal ran an experiment on the Quora duplicate-questions dataset to find how much fine-tuning data a simple question-answering use case needs, and describes how to build one on its serverless infrastructure.

March 26, 2024Tuesday
  1. Modal Blog22

    How Ramp automated receipt processing with fine-tuned LLMs on Modal

    Ramp used Modal to fine-tune its LLMs and build a text-to-structured-JSON model for receipt management, cutting receipts requiring manual intervention by 34% on infrastructure estimated 79% cheaper than major LLM providers like OpenAI. Modal functions trained many candidate models in parallel, persisted weights to Modal volumes, and served a parametrized inference endpoint. One engineer also ported a PII-stripping script for 25,000 invoices to Modal, parallelizing it on 256 cloud workers to finish in 20 minutes for $100 instead of 3 days locally.

March 14, 2024Thursday
  1. Modal Blog36

    Modal's modal-http: Serving Web Functions on Serverless Infrastructure

    Modal built modal-http, an HTTP and WebSocket stack that lets its serverless functions accept web requests, with full WebSocket support recently completed. Unlike AWS Lambda's 15-minute and 50 MB limits, Modal containers can use up to 64 CPUs, 336 GB of memory and 8 Nvidia H100 GPUs, so the team translates HTTP into function calls rather than fronting containers with a conventional reverse proxy.

January 23, 2024Tuesday
  1. Modal Blog40

    Embedding English Wikipedia in under 15 minutes with Modal and Hugging Face TEI

    Modal and Hugging Face's Text Embedding Inference server can embed the entire English Wikipedia in about 15 minutes for just over $15, using Modal's serverless GPU scaling. The post argues open-source embedding models beat API-based ones like OpenAI's text-embedding-ada-002 for production RAG, since they can be fine-tuned on user data and avoid rate limits and per-token costs.

July 21, 2023Friday
  1. Cursor Changelog62

    Cursor analyzes Llama-2-70B inference cost and latency against GPT-3.5

    Cursor measured Llama-2-70B inference on 2 80-GB A100 GPUs and found it over 3x cheaper than GPT-3.5 for prompt tokens but far more expensive for generated tokens at small batch sizes. Prompt processing costs about $0.00042 per 1K tokens versus GPT-3.5's $0.0015, while generation reaches $0.066 per 1K tokens at batch size 1 and only approaches GPT-3.5 pricing at large batches that push time to first token to several seconds. Cursor recommends open-source models for prompt-heavy tasks like classification and reranking, and closed-source APIs for generation-heavy workloads.

    Why it matters: Cursor's measured cost and latency data shows when self-hosting Llama-2-70B beats GPT-3.5 and when it does not, offering a reusable framework for open-source model serving decisions.