Hunyuan Hy3

Hunyuan Hy3 is Tencent's open-weights mixture-of-experts (MoE) model, packing 295 billion total parameters with only 21 billion active per token. Released under the permissive Apache 2.0 license, the model targets complex agentic workflows, software development, and long-horizon reasoning. It directly challenges proprietary enterprise APIs and massive open models by delivering frontier-class capabilities at a fraction of the compute overhead typical of 300B-class dense architectures.

For developers seeking a local AI model with 295B parameters, Hy3 addresses common pain points in local agent deployments: fragile tool-calling, context degradation over long horizons, and high inference latency. By pairing an active compute profile similar to a 20B dense model with a massive 256K token context window (262,144 tokens), Hy3 bridges the gap between lightweight local models that struggle with multi-step execution and monolithic models that require multi-node enterprise clusters to run at all.

Tencent trained Hy3 with an explicit emphasis on post-training refinement and reinforcement learning, drawing from real-world telemetry across dozens of production products. The result is an open checkpoint that excels where standard benchmarks often fail to measure real utility: resilient structured outputs, stable function execution across diverse agent scaffoldings, and grounded factual retrieval.

Hunyuan Hy3 relies on a sparse MoE architecture designed to maximize parameter efficiency without sacrificing generation throughput.

• Total Parameters: 295B
• Active Parameters: 21B (Top-8 routing across 192 fine-grained experts)
• Multi-Token Prediction (MTP): 1 layer with 3.8B parameters to accelerate speculative decoding and token throughput
• Transformer Layers: 80 layers (excluding the dedicated MTP layer)
• Attention Mechanism: Grouped-Query Attention (GQA) with 64 query heads and 8 key-value (KV) heads (head dimension of 128)
• Hidden Dimension: 4096 (intermediate size: 13,312)
• Context Length: 262,144 tokens (256K native)
• Vocabulary Size: 120,832 tokens
• Native Precision: BF16

The architectural highlight is Hunyuan Hy3's MoE efficiency. Rather than routing tokens to a few large experts, Hy3 routes each token to 8 active experts out of a total pool of 192. This fine-grained granularity enables specialized routing across narrow domains like syntax validation, API parsing, and logical deduction.

Because only 21B parameters compute forward passes per token, compute-bound latency remains comparable to running a compact dense model. However, practitioners must account for the memory footprint: running the model requires loading the full 295B parameter state into addressable memory, regardless of how few experts activate per token.

Hy3 also incorporates an integrated Multi-Token Prediction (MTP) layer containing 3.8B parameters. MTP trains the model to predict multiple sequential tokens simultaneously, which provides a native speedup when used with speculative decoding in optimized inference runtimes like vLLM and SGLang.

Hy3 is tuned specifically for autonomous coding environments, tool-calling pipelines, and technical document synthesis.

Tool-calling stability is often the primary point of failure when deploying local models inside agent harnesses. Hy3 addresses format drift and syntax breakages directly. Across standardized evaluations like SWE-bench Verified, Hy3 demonstrates an accuracy variance of under 4% across disparate scaffolding frameworks, including Cline, CodeBuddy, and KiloCode. The model gracefully parses multi-step function schemas, recovers from malformed tool outputs, and reliably emits valid JSON payloads without falling into repetitive correction loops.

Tencent evaluated Hy3 against domain practitioners across 270 real-world production tasks. In blind evaluations, Hy3 scored 2.67 out of 4, surpassing GLM-5.1 (2.51 out of 4). Hunyuan Hy3 for coding shines particularly in:

• Frontend Development: Generating reactive UI components, state management integration, and full landing-page prototypes.
• Infrastructure and CI/CD: Writing declarative deployment scripts, GitHub Actions workflows, and Docker compose files.
• Data and Storage Systems: Complex SQL querying, database schema migrations, and indexing strategies.

With a 256K context window, Hy3 can swallow entire code repositories, architectural designs, and regulatory documents in a single prompt. Tencent tuned the model to adhere to strict anti-hallucination guardrails: the model is trained to explicitly report missing evidence rather than extrapolate unsupported claims when analyzing large codebases or technical corpora.

Running a 295B parameter MoE locally requires significant memory capacity, but its 21B active compute profile keeps generation speeds practical once the weights are loaded.

Memory requirements depend on target quantization levels and the context buffer needed for your workload.

• BF16 (Unquantized): ~600GB to 650GB VRAM. Requires an 8x H100 (80GB) or 8x A100 (80GB) rig.
• FP8 / Q8_0: ~320GB to 350GB VRAM. Fits on 4x 96GB GPUs or a cluster of 8x 48GB cards (such as RTX 6000 Ada or A40).
• Q4_K_M (Recommended): ~175GB to 190GB VRAM. Fits comfortably on a workstation with 8x RTX 3090/4090 (24GB) GPUs, or dual/quad workstation setups with 48GB cards.
• IQ3_M / Q3_K_M: ~130GB to 145GB VRAM. Suitable for unified memory architectures with 192GB RAM (such as high-end Apple Silicon configurations).

Wondering how to run a 295B model on consumer GPU hardware? A single consumer card cannot load this model. Running Hy3 locally requires either multi-GPU PCIe clusters or unified memory workstations.

• Multi-GPU Workstations (Linux): The most practical PC build consists of multiple RTX 3090 or RTX 4090 GPUs connected across high-bandwidth PCIe lanes. Running Q4_K_M quantization requires at least 8x 24GB GPUs (192GB total VRAM) or 4x 48GB professional GPUs. Tensor parallelism (TP=4 or TP=8) is essential to split expert weights across cards.
• Apple Silicon: For non-datacenter developers, a Mac Studio or Mac Pro equipped with an M2/M3/M4 Max or Ultra chip and 192GB of unified memory is the cleanest single-box solution. Running through llama.cpp metal kernels allows a Q4_K_M or Q3_K_M quant to fit entirely inside system memory.
• Best GPU for Hunyuan Hy3: For production-grade local serving, dual or quad NVIDIA RTX 6000 Ada (48GB) cards offer the best balance of VRAM capacity, speed, and standard form factor.

The best quantization for Hunyuan Hy3 in developer environments is Q4_K_M (or standard 4-bit AWQ/GPTQ for vLLM). Because MoE routing mechanisms can be sensitive to aggressive weight pruning, going below 3 bits (such as IQ2 or Q2_K) can cause degradation in routing stability, triggering hallucinated tool calls and syntax errors in code output. Use 4-bit quantization whenever memory budgets allow.

Hunyuan Hy3 tokens per second vary significantly based on hardware architecture and interconnect bandwidth:

• 8x RTX 4090 (vLLM / SGLang, Q4 AWQ): Expect between 25 to 40 tokens per second for single-stream generation. The small 21B active compute footprint keeps token generation fast, provided PCIe bus bottlenecks do not starve expert routing.
• Apple Silicon M-Series Ultra (llama.cpp, Q4_K_M): Yields roughly 8 to 15 tokens per second. While slower than dedicated NVIDIA clusters due to memory bandwidth constraints, it remains fully usable for interactive agent workloads.

To run Hunyuan Hy3 locally with standard frameworks:

• vLLM / SGLang: Ideal for high-throughput multi-user APIs and agents running on multi-GPU Linux machines. Both runtimes offer native support for Hy3's MoE routing and GQA layouts.
• Ollama and llama.cpp: The quickest way to get started on single workstations or Apple Silicon. Once GGUF weights are converted and published, pulling the model through Ollama provides a standard OpenAI-compatible endpoint with minimal driver configuration.

Evaluating Hy3 against other leading open weights clarifies where it fits in a local deployment pipeline:

• Hunyuan Hy3 vs DeepSeek-V3: DeepSeek-V3 features a larger overall parameter count (671B total, 37B active) compared to Hy3 (295B total, 21B active). While DeepSeek-V3 edges out higher absolute scores on raw math benchmarks, its memory footprint requires at least 400GB to 500GB of VRAM even at 4-bit quantization, placing it outside the reach of 8x 24GB GPU rigs. Hy3 fits inside standard 192GB VRAM workstation configurations while matching flagship-tier performance in coding and agent interactions.
• Hunyuan Hy3 vs Qwen2.5-72B: Qwen2.5-72B is a dense model. At 4-bit quantization, Qwen2.5-72B fits in under 48GB of VRAM (two 24GB GPUs), making it much cheaper to host than Hy3. However, Hy3's 295B capacity provides noticeably stronger generalization on complex long-context reasoning, tool error recovery, and frontend code generation. Additionally, because Hy3 only computes 21B active parameters per token, its generation latency is often lower than the dense 72B model on appropriately configured hardware.

If your hardware budget maxes out at 48GB to 64GB of VRAM, dense models like Qwen2.5-72B remain the standard path. If you have a workstation or cluster with 192GB+ of memory and need reliable agentic behavior, resilient function calling, and deep technical reasoning, Hunyuan Hy3 provides enterprise-grade performance under a fully commercial Apache 2.0 license.