Kimi K1.5
Moonshot's reasoning model. Reasoning-token emission with very long thinking-block depth — sometimes 5000+ tokens per query. Strong on math; restricted commercial license.
Positioning
Kimi K1.5 is a dense 200-billion-parameter reasoning model released by Moonshot AI under the Moonshot License, a restricted commercial license. With a 200,000-token context window, it is designed for deep reasoning tasks, particularly in mathematics and complex problem-solving. The model distinguishes itself by emitting long reasoning chains—sometimes exceeding 5,000 tokens per query—making it a specialized tool for research and applications that require thorough step-by-step analysis.
Strengths
- Massive Context Window: With 200K tokens of context, Kimi K1.5 can process extremely long documents or multi-turn reasoning chains without truncation, enabling deep dives into complex problems.
- Deep Reasoning Capability: The model's architecture is optimized for generating long thinking blocks, making it particularly strong for mathematical and logical reasoning tasks that benefit from extended deliberation.
- Dense Architecture: As a dense 200B model, every parameter is active during inference, providing consistent capacity across all queries without the routing overhead of mixture-of-experts models.
- Large Parameter Count: At 200B parameters, the model has substantial representational capacity, which can translate to higher accuracy on nuanced reasoning tasks.
Limitations
- Restricted Commercial License: The Moonshot License imposes limitations on commercial use, which may hinder deployment in proprietary products or services without additional negotiation.
- Extreme Hardware Requirements: The model's size demands datacenter-grade infrastructure; even at Q4_K_M quantization, it requires ~112.5 GB of disk space plus significant overhead for KV cache and framework, far exceeding consumer or workstation GPU memory.
- High Inference Latency: The tendency to emit very long reasoning chains (thousands of tokens per query) increases time-to-first-token and overall latency, making it unsuitable for real-time or interactive applications.
- Limited Community Adoption: As a relatively new and specialized model, community benchmarks and real-world performance reports are scarce; operators should treat vendor-published metrics as best-case scenarios.
What it takes to run this locally
Kimi K1.5 is a dense 200B-parameter model. At FP16 precision, it requires approximately 400 GB of disk space. Quantization reduces this: Q8_0 ~213 GB, Q6_K ~165 GB, Q5_K_M ~142.5 GB, Q4_K_M ~112.5 GB, Q3_K_M ~97.5 GB, and Q2_K ~65 GB. In practice, add 30–50% for KV cache and framework overhead at typical context lengths. This places the model firmly in the datacenter deployment class: multi-GPU setups (e.g., 8× A100 80GB or H100 nodes) are necessary. Consumer and workstation GPUs (single 24GB or dual 24GB) cannot accommodate even the smallest quantized version with usable context.
Should you run this locally?
Yes if you have access to datacenter-grade multi-GPU infrastructure and need a dense reasoning model with a very long context window for deep mathematical or scientific research, and your use case aligns with the Moonshot License's terms.
No if you lack multi-GPU hardware, require real-time inference, need permissive commercial licensing, or are looking for a general-purpose model for chat or content generation.
Catalog cross-links
- Moonshot AI vendor page
- DeepSeek-R1 (another reasoning-focused model)
- Qwen2.5-72B (a smaller dense model for workstation deployment)
Overview
Moonshot's reasoning model. Reasoning-token emission with very long thinking-block depth — sometimes 5000+ tokens per query. Strong on math; restricted commercial license.
How to run it
Kimi K1.5 is Moonshot AI's long-context specialist model. Weights are not publicly available as of mid-2026 — API-only via Moonshot's platform. If weights release: K1.5 is known for its 128K-1M context window capability, but local deployment at those context lengths is prohibitive. At Q4_K_M, expect ~80-100 GB on disk (estimated parameter count ~100-150B). Minimum VRAM: 80 GB — single A100 80GB at Q4_K_M for 4K context. For the full 128K context, VRAM requirement explodes due to KV cache: expect 150-300 GB additional for KV cache alone. 8× H100 SXM at FP8 with vLLM is the realistic minimum for 128K context. No single-GPU consumer path for long-context use. Moonshot's architecture may not match standard Llama — ecosystem support is uncertain. Verify llama.cpp and vLLM support before provisioning hardware.
Hardware guidance
Weights not public as of mid-2026 — all guidance is speculative. If released: minimum A100 80GB at Q4_K_M for basic (4K) context. For long-context (128K): 4-8× H100 SXM at FP8. VRAM math: estimated 100-150B dense, Q4 ~60-85 GB. KV cache at 128K: ~150-300 GB depending on architecture (KV heads × layers × 2 bytes × tokens). Total at 128K: ~210-385 GB. 4× H100 80GB = 320 GB — borderline for 128K batch=1. 8× H100 = 640 GB — comfortable. RTX A6000 × 8 = 384 GB — works if architecture supported. No single consumer GPU viable. Mac Studio M4 Ultra 192 GB at Q2 + 32K context as theoretical maximum. Cloud: Moonshot API is the practical path until weights release. RunPod A100/H100 clusters as fallback.
What breaks first
- Weights not public. Primary failure mode — nothing to run locally. Moonshot API is the only supported path. 2. Long context ≠ local long context. K1.5's 128K context capability is designed for Moonshot's optimized serving infrastructure. Local deployment at 128K requires 8× H100-class GPUs. At 4K context, you lose the model's key differentiator. 3. Architecture compatibility. Moonshot's models may use custom attention kernels for long-context efficiency. Standard vLLM/llama.cpp attention backends may not support or may silently degrade. 4. KV cache precision sensitivity. Moonshot's long-context optimizations may depend on FP16 KV cache. Quantizing KV cache to FP8/INT8 could break attention quality, especially at long context.
Runtime recommendation
Common beginner mistakes
Mistake: Searching for Kimi K1.5 weights on Hugging Face. Fix: As of mid-2026, weights are not released. Check moonshot.ai for API access. Mistake: Assuming 128K context works on single A100. Fix: KV cache at 128K is 150-300 GB. Single A100 has 80 GB. Context length must be proportional to VRAM. Mistake: Treating K1.5 as a standard chat model. Fix: K1.5 is a long-context specialist — its quality on short prompts may be inferior to models trained primarily for chat. Benchmark short-context performance before adopting. Mistake: Assuming llama.cpp will "just work" with custom Moonshot architecture. Fix: Custom architectures require custom GGUF conversion scripts and model-specific kernels. Factor in 1-4 weeks of tooling work.
Family & lineage
How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.
Strengths
- Deep reasoning at frontier scale
- Strong math benchmarks
Weaknesses
- Long reasoning blocks add wall-clock cost
- Restricted license
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| AWQ-INT4 | 115.0 GB | 140 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Kimi K1.5.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Kimi K1.5?
Can I use Kimi K1.5 commercially?
What's the context length of Kimi K1.5?
Source: huggingface.co/moonshotai/Kimi-K1.5
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Kimi K1.5 runs on your specific hardware before committing money.