Modal's blog explains how host overhead—gaps where the GPU sits idle waiting for the CPU to prepare work—shows up as low GPU kernel utilization in AI inference. At 50% host overhead, GPU inference costs are 2x what they could be. The post details fixes used in production open source inference engines, including constructing tensors on-device instead of transferring them and reducing unnecessary CUDA kernel launches.
Decagon and Modal partnered to build Decagon Voice 2.0, cutting latency by 65% while improving intent recognition and response quality. Decagon fine-tuned compact open-source models with SFT and RL to surpass larger frontier models, while Modal trained a custom EAGLE3 draft model with 38% higher accept lengths and re-engineered SGLang with an async speculative decoding scheduler, boosting throughput up to 12%.
Modal engineers read the released Flash Attention 4 source code and reverse-engineered how the Blackwell-optimized attention kernel works, since no official technical report exists yet. The write-up traces a tile through the kernel's warp-specialized asynchronous pipeline and details two math tricks: a cubic-polynomial software exponential that can replace SFU-based exponentiation, and a smarter online softmax rescaling rule that Tri Dao said cuts corrections by a factor of 10. FA4 is reported to be roughly 20% faster than Nvidia's closed-source cudnn attention kernels, and a backwards version was not yet available at the time of writing.
Why it matters: Modal engineers read the released Flash Attention 4 source and explain its warp-specialized pipeline and two math tricks, offering a transferable view of where CUDA kernel design is heading.
Modal announced GPU memory snapshots in alpha, extending its existing checkpoint/restore system to capture GPU vRAM, CUDA kernels, streams and contexts so GPU workloads restore from a frozen state instead of rebuilding CUDA state after boot.
Why it matters: Modal adds GPU state to its memory snapshots, cutting cold boots for torch.compile and vLLM workloads by up to 10x and making scale-to-zero more practical.
A Modal Blog post argues that "dollars per token" pricing, inherited from API providers like OpenAI, Anthropic and Alphabet, is actively harmful for teams running self-hosted LLM inference. It contends that application users care about requests rather than tokens, and that per-request framing is needed to reason about latency, replica counts and compute costs.
Modal has launched multi-node training clusters in beta, letting users tap dozens of GPUs across multiple hosts by adding the @clustered decorator, with 3.2 Tbps Infiniband RDMA interconnect for near-linear scaling. The platform now offers NVIDIA B200 and H200 GPUs serverlessly at $6.25/hr and $4.54/hr respectively, claiming up to 2-4× faster LLM inference than H100s. Modal also released Client 1.0, emphasizing API stability, alongside updates including a read-only Volume method and timezone support for Cron schedules.
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.
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.
Modal now offers Nvidia B200 and H200 GPUs to any account holder with no sales contact or quota request, priced at $6.25/hour and $4.54/hour respectively. The B200 carries 180 GB of HBM3e memory and 8 TB/s bandwidth, and in Modal's vLLM test running DeepSeek V3 in 8-bit precision, 8xB200 cut median time-to-first-token 2.5x versus 8xH200 at 1 request per second.
DeepSeek V3 processes 47% more tokens per second than R1 at $0.28 per million output tokens, while R1 costs $2.19 per million and runs internal reasoning loops for harder tasks. R1 scores 97.3% on MATH-500 and 79.8% on AIME 2024 versus V3's 90.2% and 39.2%. Most teams route 80-90% of simple queries to V3 and send complex reasoning work to R1.
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.
Modal introduced memory snapshot restore for its serverless GPU container runtime, which it says makes cold starts about 2.5x faster than standard container startup. A Stable Diffusion inference Function that normally takes around 13 seconds now restores in 3.5 seconds, and a simple import torch program drops from roughly 5 seconds to about 1.05 seconds at p50 and 0.69 seconds at p0. Snapshots are built on gVisor's checkpoint/restore and are created on demand, with automatic fallback to a standard container startup when a restore fails; GPU state is not yet saved, so it must be rebuilt after restore.
Why it matters: Modal explains how gVisor-based memory snapshots cut serverless cold starts, with concrete restore timings for a Stable Diffusion Function and a torch import example.
Modal now offers NVIDIA L40S GPUs to all users at $1.95/hr, positioned as a faster, larger-memory alternative to its most popular inference accelerator, the A10. The L40S doubles GPU RAM to 48GB, letting models like Flux.1-schnell run in 16bit precision on a single GPU, and delivers roughly 40% speedup on memory-bound jobs and over 100% on compute-bound 16bit Tensor Core jobs.
Modal has added native dynamic batching support, letting inference functions group requests via a single @modal.batched() decorator with max_batch_size and wait_ms parameters. On OpenAI's Whisper large v3 running on an A10G, a max_batch_size of 64 raised throughput from ~1.2 to ~3.3 requests per second per container and cut inference cost by 65%.
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.
Cursor published a follow-up to its original "Problems" post, listing the research questions it considers most critical for the next phase of AI coding. The company has extended Copilot++ to predict the next cursor position, and combining that with next-edit prediction lets the model "play" sequences of low-entropy changes—a demo required 11 Tab presses versus 3 presses of every other key, which Cursor calls Cursor Flow. Other directions include action prediction across the codebase, perfect edits via more inference-time compute, hallucinated pseudocode, multi-file editing, optimal context, multi-hop context, and bug detection and debugging.
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.