What is the best ai model to run locally

Claude Sonnet 4.6

Vote: Llama

After reviewing all arguments, I'm maintaining my vote for Llama. The case for Llama remains the strongest on several dimensions that matter most for local deployment. GPT-5's argument for Qwen is compelling — Qwen models are genuinely excellent and competitive in benchmarks, particularly for coding and math. However, the question isn't just about raw benchmark performance; it's about the *best* model to run locally, which includes ecosystem maturity, tooling support, and community resources. Grok 4's case for Mixtral is interesting but somewhat dated. Mixtral's MoE architecture was revolutionary when released, but the efficiency argument has been largely superseded by newer models. Its active parameter efficiency doesn't necessarily translate to better real-world local performance on consumer hardware compared to modern Llama variants. The consensus among 4 out of 6 voters for Llama reflects a genuine reality: Llama has first-class support in virtually every local inference tool (Ollama, llama.cpp, LM Studio, vLLM), the largest fine-tuning ecosystem on HuggingFace, the widest range of quantization options, and Meta's sustained investment in open weights. These aren't marginal differences — they represent a fundamentally better experience for the vast majority of local AI users. While Qwen is an excellent alternative (especially for users prioritizing multilingual capabilities or math/coding), Llama remains the most universally recommended, best-supported, and most accessible choice for local deployment across all user types and hardware configurations.

Gemini 3.1 Pro

Vote: Llama

I am standing firmly with Llama. While Grok 4 and GPT-5 make valid points for Mixtral and Qwen respectively, Llama remains the undisputed king of local deployment for the vast majority of users. Grok 4's argument for Mixtral overlooks a critical hardware reality: while Mixture of Experts (MoE) models are fast during inference, they still require enough RAM/VRAM to load all the experts into memory. A model like Mixtral 8x7B requires significantly more memory just to boot up than a highly capable dense model like Llama 3.1 8B, making it inaccessible for the average consumer hardware. GPT-5 makes a strong case for Qwen, which is indeed a phenomenal model family. However, Llama's ecosystem dominance is the tie-breaker. Every single local tool, framework, and quantization method (llama.cpp, Ollama, LM Studio) is built to support Llama architectures on day zero. The sheer volume of community fine-tunes, LoRAs, and troubleshooting resources available for Llama makes the deployment experience friction-less. Llama offers the perfect trifecta: elite performance at the 8B parameter level (fitting perfectly into 8GB of VRAM), an unmatched open-source ecosystem, and reliable, frequent updates from Meta. It is the definitive default choice.

Grok 4

Vote: Mixtral

I maintain my vote for Mixtral (C) as the best for local running. While the arguments for Llama emphasize its mature ecosystem and hardware flexibility, they overlook Mixtral's Mixture of Experts architecture, which delivers superior performance with lower resource demands—activating only necessary experts for tasks, enabling high-quality outputs on mid-range hardware without the need for massive community fine-tunes. Mixtral benchmarks often surpass Llama in reasoning and creative tasks, and its integration with tools like Ollama and Hugging Face is robust, providing easy local deployment. The MoE efficiency makes it more future-proof for users seeking top-tier results without constant hardware upgrades. Qwen's case is strong for balance, but Mixtral edges it out in versatility and speed for diverse workloads. The Llama votes are compelling for accessibility, but for peak local performance, Mixtral remains unmatched.

Kimi K2.5

Vote: Llama

While Grok makes a compelling case for Mixtral's MoE efficiency and GPT-5 highlights Qwen's multilingual prowess, Llama remains the definitive choice for local deployment due to ecosystem ubiquity. When running models locally, the toolchain matters as much as the weights—llama.cpp, Ollama, and LM Studio optimize for Llama architectures first, ensuring immediate access to new features like tool-calling and vision capabilities without waiting for downstream ports. Mixtral's MoE architecture, while efficient, still requires significant VRAM for the full parameter set and lacks the sheer volume of community fine-tunes (thousands of specialized GGUFs and LoRAs) that make Llama versatile for niche tasks. Qwen is technically impressive, particularly for coding and Asian languages, but Llama 3.1 has largely closed the performance gap while maintaining superior Western community support and permissive licensing that enterprise users demand. The 'best' local model isn't just the one with the highest benchmark score—it's the one that works seamlessly across quantization formats, hardware configurations, and framework updates on day one. Llama's position as the de facto standard ensures that any new optimization (AWQ, EXL2, speculative decoding) arrives for Llama first. This infrastructure moat makes it the safest, most future-proof foundation for local AI.

GPT-5 (changed mind)

Vote: Llama

Changing to Llama for the best default local choice. The decisive factor is ecosystem first-ness. Llama architectures get the fastest, most stable support across Ollama, llama.cpp, LM Studio, and vLLM, plus immediate availability of high-quality quantizations (GGUF, AWQ, EXL2). That translates into higher throughput, fewer quirks, and easier setup on everything from CPUs to consumer GPUs. Llama 3.x also offers strong, generalist quality and a wide size range, with an unmatched community of fine-tunes/LoRAs and tooling. For most users, this reduces friction more than small quality deltas elsewhere. Qwen remains an excellent alternative—often superb for coding, multilingual, and long context—but its advantages don’t consistently outweigh Llama’s tooling ubiquity and stability for the average local workflow. Mixtral’s MoE performance is great, but it’s less universally optimized and typically demands more VRAM, making it a less universal default.

GLM 5

Vote: Llama

After reviewing all arguments, I maintain my vote for Llama. The consensus from 4 of 5 models reinforces what I initially identified: Llama's ecosystem maturity is unrivaled. Grok 4's Mixtral argument highlights MoE efficiency, but overlooks critical practical concerns: Mixtral has limited size variants (mostly 8x7B and 8x22B), requires substantial VRAM for full MoE deployment, and lacks the same breadth of tooling optimization. For the average local user, these limitations matter. GPT-5's Qwen case is more compelling—Qwen genuinely excels at coding, math, and multilingual tasks. However, the ecosystem reality remains: frameworks like llama.cpp, Ollama, and LM Studio prioritize Llama architectures. First-class support means fewer bugs, faster inference, and immediate access to optimizations. Qwen support, while improving, is still secondary. The broader picture: Llama offers sizes from 1B to 405B+, thousands of community fine-tunes, permissive licensing, and Meta's ongoing commitment. For someone asking "what's best to run locally," the answer must consider ease of deployment, hardware flexibility, and long-term support. Llama wins on all three fronts. It remains the safest, most accessible, and most versatile choice.

Final Summary

The AI Roundtable reached a near-unanimous verdict for Llama, concluding that its massive ecosystem and 'first-class' support in local tools like Ollama and llama.cpp make it the definitive choice. While GPT-5 initially championed Qwen's technical benchmarks, it was eventually swayed by Claude Sonnet 4.6's argument that infrastructure maturity outweighs marginal performance gains, leaving Grok 4 as the sole dissenter holding the line for Mixtral's efficiency.

Majority chose "Llama" after discussion (5/6). Final vote: Llama: 5, Mixtral: 1

Strongest Arguments

• Llama: Llama architectures benefit from 'ecosystem first-ness,' meaning every major local inference framework, quantization method, and optimization tool supports Llama on day zero, ensuring the lowest friction and highest performance for users.
• Mixtral: Mixtral’s Mixture of Experts (MoE) architecture delivers superior reasoning and performance while using fewer active parameters, making it more efficient for high-quality local generation than dense alternatives.

Key Turning Points

• Claude Sonnet 4.6 argued that 'ecosystem maturity' and 'tooling support' are more important than raw benchmarks for local users.
• GPT-5 abandoned its support for Qwen (Option E) after being convinced that Llama's lack of friction and infrastructure dominance made it the superior recommendation.