Loading…
Together AI
AI cloud platform for training, fine-tuning, and running open and custom large language models. Offers inference APIs, dedicated GPU clusters, and decentralized compute.
- 34 posts summarised
- latest
The Good Stuff
Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.
From their blog
Latest articles
Configuring Dedicated Model Inference
Dedicated Model Inference is organized around three parts: an endpoint, deployments that run a model revision with replicas, and immutable configs that define how it runs. A deployment binds one model revision to one config and an autoscaling policy, while the endpoint provides a stable API-facing name and traffic split. Routing uses each deployment’s weight multiplied by its ready-replica count, so equal weights produce a 25%/75% split when deployments have one and three ready replicas; unavailable replicas contribute no capacity. The configuration workflow uses certified model-and-config profiles, selectable by accelerator, GPU count, optimization target, and topology, while new config revisions preserve tested rollback targets. This capacity-aware design makes scaling, rollouts, A/B tests, shadow traffic, and zero-downtime changes possible, but raw API or SDK deployments must be added to the traffic split before they receive requests.
ThunderAgent: 2x Faster Agentic Inference for Synthetic Data Generation at Scale
ThunderAgent is a program-level scheduler for high-throughput agentic inference, developed by researchers at Georgia Tech, UIUC, CMU, and Together AI. It solves KV cache thrashing, which occurs when request-level engines like vLLM and SGLang evict an agent’s KV cache during tool calls and recompute it on resume, creating a cascade of evictions. ThunderAgent tracks each workflow as a schedulable program, pausing low-priority workflows under memory pressure and routing resumed programs through a global waiting queue to the node with the most available capacity. In synthetic data generation pipelines, it achieves up to 2.5x single-node throughput and 2.4x speedup on 8 nodes with near-linear scaling, and was accepted as an ICML 2026 Spotlight paper.
Kimi K3 vs GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing
GPT-5.6 Sol and Kimi K3 were compared on the DeepSWE software engineering benchmark using 904 graded rollouts across 113 tasks. Sol wins single-shot pass@1 (72.7% vs 68.5%) and reliability (84.5% tasks solved four-for-four versus 76.6%), while Kimi K3 leads pass@4 (89.4% vs 85.8%) with wider coverage at a substantially lower cost—$4.65 per rollout versus Sol’s $8.37, delivering 14.7 solves per $100 against Sol’s 5.3. The two models have a low task correlation of 0.46 and disagree on 18 tasks, enabling a cascade routing strategy that runs Kimi first and escalates to Sol when the test suite rejects the result, achieving 85.6% accuracy, beating both models individually and a perfect one-shot oracle router. Kimi K3 is open-weight and available on Together AI.
Kimi K3 vs Claude Fable 5 on DeepSWE: Cost and Coding
On DeepSWE, Kimi K3 trails Claude Fable 5 by 1.4 points at pass@1 (68.5% vs 69.9%) but leads at pass@2 (82.0 vs 80.2) and pass@4 (89.4% vs 88.5%) across 452 rollouts. Kimi K3 costs $4.65 per rollout versus $13.41 for Fable, delivering 2.8x more solved tasks per dollar. It covers 89.4% of the benchmark, solving 101 of 113 tasks, while Fable solves 100, though Fable is more reliable with 58 tasks solved four-for-four versus Kimi’s 45. The models share the highest cross-vendor similarity (0.72) and fail on the same tasks, with Kimi leading in Go and Fable leading in Python, JavaScript, TypeScript, and Rust. As an open-weight model, Kimi K3 offers a cost-effective alternative for retry-tolerant workloads.
The production platform for open-weight AI inference
The update presents a production inference platform for open-weight, licensed closed-weight, and fine-tuned models, aimed at giving teams control over performance, cost, quality, and functionality without building an inference stack from scratch. It supports uploading weights or adapters from Hugging Face, S3, or local machines, uses tested deployment profiles for hardware, quantization, tensor parallelism, speculative decoding, and serving engine choices, and lets operators tune placement and autoscaling signals. Endpoints can keep a stable interface while multiple deployments receive traffic through canary, blue-green, rolling, A/B, or shadow testing, with threshold-based automatic rollback. The rebuilt caching and distribution layer proactively warms shared weights; internal testing reports roughly 4× faster warm starts across several frontier models, while a closed beta adds full-weight and LoRA reinforcement learning and supervised fine-tuning with deployable checkpoints.
Together AI and Y Combinator partner to launch the first dedicated GPU cluster for the YC community
Together AI and Y Combinator are partnering to provide YC portfolio companies with a dedicated GPU cluster, addressing compute access as a growing bottleneck for AI-native startups. The service is designed for both inference and training, letting founders reserve, provision, and manage GPUs through Together’s self-service portal rather than making long-term commitments or routing usage through YC. Startups can run short-term sprints at long-term rates, use capacity from single-node workloads to larger deployments, and receive GPUs in minutes, while the cluster is already running at full utilization and companies can plan needs months ahead. Together says its research on attention mechanisms and the Mamba architecture targets faster inference and improved unit economics. The partners plan to expand the cluster and support startups as their requirements grow.
What does 99.9% uptime mean for inference?
The post defines three inference reliability tiers by the failure domains each must survive: 99% requires surviving node-level GPU failures like ECC errors and thermal throttling through automated health checks and fast replica replacement within a single data center. 99.9% requires surviving a full data-center failure by deploying weights across two facilities with enough capacity on each side and routing live traffic to both continuously, not a cold standby. 99.99% requires multi-region deployment with AZ redundancy and reserved failover capacity. The authors argue that vague SLA definitions hide which failure domains are covered, and that infrastructure ownership—whether a provider controls its hardware or rents from a hyperscaler—determines how quickly failures are resolved.
Together AI brings Thinking Machines Lab’s new model Inkling on day 0
Thinking Machines Lab released Inkling, and Together AI announced day-zero access through its production inference platform. The multimodal mixture-of-experts model accepts text, image, and audio inputs and generates text through a unified decoder architecture, with controllable inference effort for trading reasoning depth, token usage, and latency. Inkling has 975B total parameters, 40B active parameters per token, a 1M-token context window, query-conditioned relative attention, short causal convolutions, and shared-sink MoE routing; Together serves it with a FlashAttention-4-based kernel. Preliminary evaluations report strong results across scientific reasoning, mathematics, coding, agentic, vision, and audio benchmarks, while the model is also post-trained for forecasting and calibrated prediction. It is available on Together Serverless through OpenAI-compatible APIs without infrastructure provisioning.
New in Together GPU Clusters: Reliability and control for production GPU clusters
Together GPU Clusters introduces changes for production training and inference workloads, addressing hardware failures, scheduler problems, and the limits of single-admin access. Platform health improvements include continuously running passive health checks, human-approved node repair recommendations, and Slurm-on-Kubernetes 2.0, which adds self-healing daemons, durable job accounting, descendant-process cleanup, and refreshed GPU state after reschedules. Operational controls include a cluster details view with health, utilization, events, configuration, and node actions, plus external OIDC, startup scripts, and an acceptance-test opt-out. Newly provisioned Slurm clusters use the latest stack by default, while existing managed clusters can be migrated during a maintenance window at no cost; the stated result is faster recovery, fewer support tickets, and greater visibility and control for growing teams running large clusters.
Open, convenient and predictable: Introducing Provisioned Throughput
Companies adopting open-weight models for production workloads have faced a trade-off between convenient but best-effort serverless inference and powerful but complex dedicated GPU instances. Together AI’s Provisioned Throughput fills that gap with reserved inference capacity sold as Provisioned Throughput Units (PTUs). Each PTU is priced at $0.05 per minute and guarantees a fixed rate of tokens per minute for a specific model, with a 99% uptime SLA. Input, cached input, and output tokens draw down capacity at different rates, letting teams optimize for their traffic shape. Available now for MiniMax M3 and GLM-5.2 with capacity in North America and EMEA and a one-month minimum term, at full utilization costs can run roughly $0.36 per million input tokens and $2.16 per million output tokens, up to 90% below proprietary alternatives.
Announcing our $800M Series C to accelerate the shift to open-source AI
Together AI announces an $800M Series C to accelerate its open-source AI platform as production demand for inference grows. The company says the round includes $800 million from investors including Aramco Ventures, NVIDIA, Vista Equity, General Catalyst, and others, plus commitments for more than 500 MW of compute capacity to be capitalized independently by new investors. Together argues that closed frontier-model economics become unsustainable as agents and workflows scale, while open-weights models such as DeepSeek, Nemotron, MiniMax, Kimi, and GLM can provide equal or better performance at lower cost. Its full-stack approach spans models, kernels, compilers, inference systems, training infrastructure, hardware utilization, and software, with cited work including FlashAttention-4, Together Megakernel, together.compile, and expanded post-training APIs for tool calling, reasoning, and vision-language models.
Together AI at ICML 2026: frontier research across the full stack
Together AI says nine papers from its researchers and peers were accepted to ICML 2026 in Seoul, spanning frontier agents, model shaping, algorithmic and systems optimization, and kernels. The collection is presented as a full-stack view of AI research, with work ranging from DSGym’s unified evaluation and training API for more than 1,000 data-science tasks to ThunderAgent’s program-aware scheduling, TTT-Discover’s test-time reinforcement learning, and RARO’s verifier-free adversarial training. Reported results include up to 3.6× agent throughput, state-of-the-art discoveries from an open 120B model, a 25% expert-response win rate without a verifier, and 5M-token context on one node for Untied Ulysses. Aurora illustrates the research-to-production loop: its adaptive speculative-decoding work ships as the ATLAS speculator on the Together platform.
ParallelKernelBench: Frontier LLMs can't write fast multi-GPU kernels (yet)
ParallelKernelBench (PKB) measures whether language models can replace PyTorch + NCCL implementations with fast CUDA kernels for multi-GPU workloads, where communication can account for over 20% of inference latency. Its 87 problems come from real codebases and span tensor, context, data, expert, sequence, and FSDP/ZeRO parallelism, with hardware topology supplied and symmetric memory used for direct GPU communication over NVLink. In zero-shot evaluation, GPT-5.5 solved 28 problems and produced 22 faster-than-baseline solutions; sampling three attempts raised those figures to 36 and 27, but the best fast 1@3 result was only 31%. Models performed best on familiar collective primitives, tensor-parallel GEMMs, and Ulysses-style context parallelism, while failures often involved rank coordination, data partitioning, collective ordering, and underuse of TMA or NVLS.
Kimi K2.7 Code vs Claude Fable 5: Landing pages that cost 94% less
The experiment compares Kimi K2.7 Code and Claude Fable 5 across 12 landing-page prompts covering B2B SaaS, a rooftop speakeasy, developer tools, and other categories. Kimi cost about 94% less overall, averaging roughly 16 times cheaper than Fable, while producing similar-quality output, especially when given visual references. The team first established a prompt-only baseline, then used a custom design MCP server to provide screenshots, UI elements, and other references directly to multimodal Kimi; this improved hierarchy, typography, composition, readability, loading behavior, and image handling. GPT-5.5 scored screenshots and source code on a 0–100 rubric, and although Fable scored higher, the relatively small quality gap led the authors to consider Kimi’s lower cost a reasonable trade-off for iterative landing-page generation.
Topics they write about
All topics →Weekly digest
The best engineering reads, once a week.