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Kimi K3 Model Deployment and API

31 posts · May 2026 – Aug 2026

What this trail covers

Fireworks AI now offers the Kimi K3 model natively on its platform, enabling unified retrieval-to-response pipelines. This integration allows users to run embedding, retrieval, reranking, and generation on a single platform, reducing latency, cost, and security surface. The platform supports Kimi K3, optimized for various use cases including coding, writing, and multimodal analysis, with advanced features like Delta Attention for efficient large context handling. Fireworks provides US-hosted inference endpoints and offers tiered serverless options (Standard, Priority, Fast) for different reliability and performance needs. Fine-tuning with LoRA is supported for custom model deployment. On-demand deployments are available for dedicated GPU usage without rate limits.

All Fireworks AI trails
  1. Fireworks AIThis post introduces DeepSeek V4 Pro 0813 as a new model available on the Fireworks AI platform. It details a rigorous evaluation of DeepSeek V4 Pro using the CyberGym benchmark, comparing its performance against Kimi K3, GPT-5.5, and Claude Opus 4.8. The analysis highlights DeepSeek V4 Pro's zero refusal rate on adversarial security tasks, its cost-effectiveness (half the cost per success of the highest-scoring model), and its ability to outperform closed-source models like GPT-5.5 in both accuracy and price for security agent workloads. The post emphasizes the importance of open models for high-volume security agents due to the 'refusal walls' of closed models and provides specific cost and reward metrics for each tested model.
  2. Can open models carry readable silent signals before they speak? Reproducing J-Lens Readouts on Kimi K3 & Qwen3.5-9BThis post details the application of the Jacobian Lens (J-Lens) technique to Kimi K3 and Qwen3.5-9B models hosted on Fireworks AI. It demonstrates how J-Lens can be used to probe the internal states of these models to identify 'silent signals'—vocabulary associated with the final output that appears before the model generates its response. The post describes experiments, including a 'Paired Copy Experiment' and extending tests to eight more episodes, to show that these silent signals reflect the model's intended focus even when the visible output is the same. It also discusses the challenges encountered, such as tokenizer limitations, and highlights the technical requirements for running such analyses.
  3. Fireworks AIIntroduced support for Meta's Muse Glimmer model, a 30B dense model with advanced architectural features like grouped-query attention, a perception encoder for multimodal understanding, and a large context window. Highlighted its suitability for agentic workflows due to sliding-window attention and efficient KV cache, enabling complex multi-turn tool orchestrations. Detailed its performance on benchmarks relevant to agentic tasks and provided recommended inference settings, including reasoning effort control via system prompts.
  4. Your AI Performance Stack is Fireworks Models with Voyage AI embeddingsThis post details the integration of Voyage AI's embedding, reranking, and multimodal models with Fireworks' inference platform. It highlights the benefits of a unified retrieval-to-response pipeline, including reduced latency, cost, and security surface. Specific Voyage models (voyage-4-large, voyage-4, voyage-4-lite, voyage-4-nano, voyage-multimodal-3.5, rerank-2.5) are discussed for different use cases, from high-accuracy customer support to cost-optimized internal assistants and agentic systems. The post also emphasizes the ability to combine these retrieval capabilities with post-trained open-source models on Fireworks for specialized intelligence and introduces multimodal RAG capabilities with voyage-multimodal-3.5.
  5. Three Tests to Run Before You Switch from LoRA to FullFTThis post details experiments comparing LoRA and Full Parameter Fine-Tuning (FullFT) on Qwen3.5-9B, focusing on three key areas that influence performance: data coverage, optimization (learning rate tuning), and adapter capacity (rank). It provides a framework for engineers to test these factors before deciding to switch from LoRA to FullFT, demonstrating that data diversity and learning rate optimization can often close performance gaps previously attributed solely to adapter size. Specific experiments on 'Placement', 'Register Allocation', and 'Nexa VM' tasks illustrate these findings.
  7. Fine-Tune Your Own Embedding Model from an LLM — for the Price of a CoffeeThis post introduces a practical and cost-effective method for fine-tuning LLM-based embedding models on the Fireworks platform. It details the use of contrastive learning with In-Batch Negatives and InfoNCE loss to adapt general-purpose embedding models (like Qwen3-Embedding-8B) to specific domains. The post presents experimental results showing significant improvements in retrieval tasks (e.g., +36% nDCG@10 on legal citation retrieval) and discusses three training modes: full-parameter updates, LoRA adapters, and a hybrid approach, emphasizing the benefits of adapting existing models over training from scratch.
  8. Kimi K3 on Fireworks: Frontier Intelligence You Can OwnThis post announces the availability of Kimi K3 on the Fireworks AI platform, highlighting its frontier-level intelligence and cost-efficiency compared to closed models. It details Kimi K3's performance on various benchmarks (coding, writing, legal analysis, multimodal) and introduces Kimi Delta Attention, a custom kernel adaptation for efficient large context processing. The post emphasizes the benefits of serving and fine-tuning Kimi K3 on Fireworks, including US-hosted inference, zero data retention, and the ability to start training from a laptop. It also contrasts Kimi K3's cost-effectiveness with Opus 5, showing a significant advantage per task.
  9. Fireworks AIIntroduced Fireworks Nexus, a platform for managing and routing AI workloads to optimize costs and maintain developer experience. Key components include: 1. Enterprise Controls & Cost Observability for budget setting, ROI tracking, and policy enforcement on a production inference platform with US-hosted endpoints and zero data retention. 2. Workflow Continuity via FireConnect, an open-sourced tool (Apache 2.0) that maps models based on harness configurations and provides OpenAI/Anthropic API compatibility. 3. Intelligent Traffic Management (FireRouter) using a custom-trained model to route requests to cost-effective open-weight models (like Kimi-K3, GLM-5.2) or existing providers based on request difficulty, aiming for 3-5x cost reduction. The post also highlights independent evaluations by Faros and Arize demonstrating cost savings and quality preservation.
  10. Fireworks AIIntroduces LoRA training and serving capabilities for the Kimi K3 model. Details the cost-efficiency of LoRA adapters for fine-tuning, the serverless training model with pay-per-token pricing, and the deployment options of live merge and multi-LoRA. Provides examples of training Kimi K3 for the Countdown and Frozen Lake tasks, demonstrating the effectiveness of LoRA for teaching new objectives and step-by-step problem-solving.
  11. Trilogy’s Playbook for Open-Weight Cybersecurity with Kimi K3This post details the integration of Kimi K3 on Fireworks AI for cybersecurity use cases, building upon previous work with Trilogy. It highlights the advantages of open-weight models for defensive AI workloads, emphasizing cost-effectiveness and deployment flexibility. The post outlines a practical reference stack that combines deterministic tools (indexing, parsing, deduplication) with Kimi K3 for judgment (evaluation, prioritization, remediation). It also describes a path-centric audit workflow that maps entry points, call graphs, and security-sensitive sinks, using Kimi K3 to evaluate reachability and impact within bounded units of work. The integration with Fireworks' OpenAI-compatible API is emphasized for seamless incorporation into existing clients and agent frameworks, allowing teams to scale inference independently.
  12. Kimi K3 is competitive with Fable; Kimi K3 + Fable is SoTA.This post provides a detailed technical comparison of Kimi K3 against Fable across various task types (SWE, Terminal, Algorithmic, Multi-Language, Legal). It quantifies the performance differences, analyzes cost implications based on token usage and task complexity, and demonstrates the benefits of a routing strategy for optimizing cost and quality. Specific metrics include task solve rates, turns per task, and token counts, illustrating how Kimi K3's cost-effectiveness is achieved through efficient processing and prompt caching, even when handling more tokens.
  13. Heidi x Fireworks: Bridging the Gap in Frontier Model PerformanceThis post details the technical collaboration with Heidi Health, focusing on achieving superior frontier model performance. Key contributions include: 1. Demonstrating Supervised Fine-Tuning (SFT) to match foundation model behavior and Reinforcement Fine-Tuning (RFT) for 'deep thinking' by incorporating preference signals. 2. Highlighting the critical role of data quality through synthetic rewrites and LLM-as-a-Judge filtering for de-noising preference data. 3. Emphasizing the necessity of scaling effective batch sizes to over 1.5 million tokens using gradient accumulation to stabilize training and improve win rates against proprietary models. The partnership resulted in a 3.5x reduction in latency.
  14. Optimizing MiniMax M3 Sparse Attention on NVIDIA BlackwellThis post details the engineering effort to optimize the MiniMax M3 sparse attention mechanism on NVIDIA Blackwell GPUs. It introduces a KV-stationary execution path, analyzes the performance trade-offs between Q-outer and KV-outer approaches, and describes specific optimizations for the attention store and load balancing. The resulting kernel achieves significant throughput improvements and speedups over existing baselines.
  15. How Gumloop Scaled Open-Weight Model Usage 7x in 3 Weeks with Fireworks AIThis post details how Gumloop scaled its use of open-weight models by 7x in three weeks by integrating with Fireworks AI's inference platform. It highlights the successful migration of a critical internal agent from a proprietary model (Opus 4.8) to an open-weight model (GLM-5.2) without user-perceptible degradation, leading to significant cost savings (up to 72%). The post emphasizes Fireworks AI's reliability as a key factor in Gumloop's decision to adopt open-weight models for production workloads, enabling them to build production-ready AI agent workflows with improved economics.
  16. Open, frontier, and yours: LangChain Deep Agents on NVIDIA Nemotron 3 Ultra, running on FireworksThis post details the integration of NVIDIA Nemotron 3 Ultra with LangChain Deep Agents, emphasizing cost-per-task optimization for agentic workloads. It highlights Fireworks' inference stack, including NVIDIA Blackwell support and FireAttention kernels, for high-throughput and low-latency inference. The post also discusses the capability for enterprises to post-train Nemotron 3 Ultra on Fireworks for specialized intelligence, enabling ownership of AI models and competitive advantage.
  17. Best Open Source LLMs in 2026: We Reviewed 7 ModelsThis post evaluates and compares seven open-source LLMs (GLM 5.2, Kimi K3, Kimi K2.7 Code, DeepSeek-V4-Pro, DeepSeek-V4-Flash, MiniMax M3, Qwen3.7 Plus, gpt-oss-120b, and Gemma 4 31B IT) based on benchmark quality, context window, modality support, and license terms. It highlights Kimi K3 as a benchmark leader awaiting full weights and listing, and GLM 5.2 as the leading open model available on Fireworks for broad reasoning, coding, and long-context agents. The post details the evaluation methodology, including reasoning and knowledge, software engineering performance, and provides key takeaways for selecting models based on specific use cases and constraints. It also includes a section on 'Why run these models on Fireworks,' emphasizing performance at scale and deployment options.
  18. GLM 5.2 Fast is live on FireworksIntroduced GLM 5.2 Fast, a new serving path for GLM 5.2 that achieves 2-3x higher inference throughput than the Standard path on shared serverless infrastructure. This is enabled by architectural optimizations including advanced Mixture-of-Experts (MoE) sharding strategies, sparse attention serving, and speculative decoding. The post details the trade-offs in MoE and attention sharding, emphasizing that different parallelism strategies are chosen independently based on workload characteristics. It also highlights the continued support for the full 1M-token context window, production-ready features like structured output modes and tool calling, and aggressive prompt-caching pricing to ensure speed does not compromise reliability or cost-effectiveness for agentic workloads.
  19. Fireworks AIThis post details how Fireworks provides the inference and rollout layer for large-scale distributed reinforcement learning, enabling Cursor to run RL across multiple global clusters. Key capabilities include cross-region model updates with significant transfer size optimization, minutes-level synchronization staleness, stable rollout fleets for MoE models, low-latency inference during training and evaluation, and reuse of production inference for RL sampling. This allows for accelerated RL cycles without dedicated inference infrastructure.
  20. How Factory Grew Open Model Usage 2-3x in Six Months on FireworksThis post details how Factory leveraged Fireworks AI to achieve 2-3x growth in open model usage within six months, driven by cost efficiency and vendor lock-in avoidance. Fireworks provides day-zero access to a broad range of open models with per-model fidelity, reliable performance, and managed infrastructure, enabling Factory to offer diverse model choices and optimize costs for its customers through features like Factory Router.
  21. GLM 5.2 is live on Fireworks inference, day zero.Announced the availability of GLM 5.2 on the Fireworks inference platform, highlighting its performance on coding benchmarks and its 1M token context window for long-horizon agentic tasks. Detailed Fireworks' infrastructure advantages as a direct inference provider, emphasizing control, data privacy, and SLAs. Included customer testimonials and integration details for accessing GLM 5.2 via various APIs and tools.
  22. Qwen 3.7 Plus is now live on FireworksAnnounced the availability of Qwen 3.7 Plus on Fireworks' serverless infrastructure. Detailed its multimodal capabilities, reasoning modes, and context window. Highlighted Fireworks' role as a direct inference provider with specific performance and data handling commitments. Provided integration details for popular coding agents and API endpoints. Emphasized the distinction between Fireworks as an inference provider versus API routers.
  23. Kimi K2.7 Code on Fireworks: Better Agents, Lower Cost per Task, Available Day-0 | Fireworks AILaunched Day-0 support for Kimi K2.7 Code, highlighting its efficiency gains (30% fewer reasoning tokens) and improved benchmark performance. Detailed the cost implications of reduced reasoning tokens for agentic workflows, emphasizing task completion cost over per-token pricing. Introduced and explained the Standard, Priority, and Fast serving tiers on Fireworks serverless infrastructure, detailing their use cases for agentic traffic.
  24. MiniMax M3 is live: long context + native multimodality at 1/20th the priceAdded support for MiniMax M3, a new frontier model featuring long context (up to 500K tokens at launch, with 1M planned) and native multimodality. Highlighted the underlying MiniMax Sparse Attention (MSA) architecture and its performance benefits (e.g., 15x faster decoding at long context). Detailed M3's capabilities in coding and agentic tasks, and updated pricing information to include M3 alongside M2.7, with specific considerations for long-context pricing tiers.
  25. Trilogy Validates Open-Weight AI Models for Enterprise Workloads with Fireworks AIThis post details Trilogy's validation of open-weight AI models for enterprise workloads using Fireworks AI. It highlights the challenges of scaling AI adoption, including escalating infrastructure costs and operational constraints like rate limits and usage caps. Trilogy selected Fireworks as a primary inference infrastructure layer for evaluation and early production workloads, enabling a shift from fragmented model experimentation to standardized evaluation and production-grade testing. Key results include standardized open-weight model usage, enabled billion-token scale agentic workflows (e.g., OpenSymphony), and reduced inference costs (~1/5th of proprietary systems). The post emphasizes Fireworks' role in providing production-grade inference infrastructure for open-weight models, allowing for rapid iteration, stable inference under high throughput, and a direct path from evaluation to production-grade testing.
  26. Accelerate Developer Output Across Your EnterpriseThis post details the technical aspects of Fireworks AI's code assistance product, emphasizing context-aware code generation, inline fixes, and real-time autocomplete. It highlights low-latency performance through streaming completions with speculative decoding for sub-100ms response times, and scalable infrastructure leveraging GPU autoscaling and batching for cost-efficient handling of millions of concurrent requests. The post also quantifies improvements in developer output, including 2X faster code generation, 30% lower latency at scale, and 2.5X higher fix acceptance rates. It mentions the use of various models optimized for different tasks, such as Qwen3, Kimi K2 Instruct, and Deepseek models, and discusses fine-tuning capabilities for domain-specific accuracy. The technical depth is evident in the discussion of performance metrics and infrastructure scaling.
  27. Enterprise Agentic Systems for Multi-Step Workflow AutomationThis post introduces Enterprise Agentic Systems, a new capability focused on enabling AI agents to perform multi-step workflows and interact with tools. Key technical contributions include: structured function calls for reliable tool invocation, support for multi-function and nested workflows, voice-to-action pipelines for speech-to-action conversion, fine-tuning capabilities with FireOptimizer for domain-specific language and workflows, and scalable infrastructure with GPU autoscaling for low-latency, high-concurrency inference. The post also quantifies performance improvements in terms of win rate, throughput, speed, and cost efficiency, and mentions specific model integrations like Qwen3 and Llama 3.1.
  28. Kimi K3 API & Playground | Fireworks AIThis post announces the availability of the Kimi K3 model on the Fireworks AI platform. It details the model's specifications (2.81T parameters, 1M token context window, native visual understanding via Kimi Delta Attention) and its availability across different serverless tiers (Standard, Priority, Fast) with associated pricing and performance characteristics. It also highlights support for fine-tuning using LoRA and on-demand deployments on dedicated GPUs. The post emphasizes the model's suitability for various use cases and its availability via US-only endpoints for regulated industries, with zero data retention by default.
  29. UndatedDeepSeek-V4-Pro-0813 API & Playground | Fireworks AIAnnounces the official release of DeepSeek-V4-Pro-0813, superseding the preview version. Highlights enhanced agentic capabilities and performance improvements, particularly in production environments. Mentions it's built on the DeepSeek-V4-Pro (Preview) model structure with an attached DSpark speculative decoding module. Details API features including fine-tuning (LoRA), serverless API (pay per token), and on-demand deployment (dedicated GPUs, no rate limits). Provides pricing for serverless usage and model specifications (1.6T parameters, 1040k context length, Mixture-of-Experts).