Observed Signal · Apr 13, 2026 · Technical Guide · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Best Ollama Models for OpenClaw Bazaar Skills Locally
This technical guide ranks Ollama-compatible local models for running OpenClaw Bazaar marketplace skills, based on hands-on testing across dozens of skill categories. It explains the four critical model requirements for reliable skill execution—tool-calling support, 64K+ token context windows, strict instruction adherence, and adequate inference speed—and cites BFCL-V4 as the primary benchmark for tool-call reliability. The article places Qwen3.5 27B and Qwen3 Coder Plus 72B in a premium Tier 1, highlights Qwen3.5 35B-A3B (MoE) and GLM-4.7 Flash as strong mid-tier choices, and identifies Qwen3.5 9B as an entry-level option. It lists models and configurations to avoid, provides VRAM and hardware guidance, offers Ollama configuration tips (use the native API baseUrl and disable reasoning mode when needed), and recommends a hybrid local-plus-cloud strategy for complex skills.
Practical, hands-on guide that helps developers run agentic Bazaar skills locally (cost, privacy, and performance implications), but it is a niche technical resource rather than industry-shifting news.
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Key Takeaways & Evidence Grounding
- The guide ranks Ollama models for executing OpenClaw Bazaar skills based on real-world testing across dozens of skill categories.
- Four requirements for Bazaar skill compatibility are listed: tool calling support, 64K+ token context windows, precise instruction adherence, and adequate inference speed.
- BFCL-V4 (Berkeley Function Calling Leaderboard) is used as the primary benchmark for tool-call reliability; Qwen3.5 27B scored 72.2 on BFCL-V4 according to the guide.
- Tier 1 recommended models: Qwen3.5 27B (~20 GB VRAM, 128K context) and Qwen3 Coder Plus 72B (48 GB+ VRAM, 128K context).
- Configuration advice: use Ollama native API at http://localhost:11434 (avoid the /v1 OpenAI-compatible endpoint) and disable reasoning mode for models that produce internal chains-of-thought.
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Qwen 3.5 Wins Local Benchmark Using llama.cpp
An independent Round 3 benchmark replaced Ollama with a direct llama.cpp server to measure local LLM performance more precisely. The author built a 12-task automated suite across five categories (coding, multi-file agentic coding, reasoning, tool use, and speed) and tested five models: Qwen 3.5, Gemma 4, Devstral, Codestral, and DeepSeek R1. Running on an NVIDIA RTX 5090 system, Qwen 3.5 swept the leaderboard—best coding, best agentic performance, and best single-model weighted score—reaching ~206.7 tokens/sec and a weighted overall score of 85.3. The migration reclaimed ~44 GB of disk from Ollama, enabled fine-grained inference flags (e.g., --reasoning-budget, --chat-template chatml), and highlighted Mixture-of-Experts (MoE) models’ throughput advantage for local deployment.
Local LLMs Reach Practical Usability
A developer revisits running large language models locally and reports that the landscape has shifted: newer Qwen models (dense and MoE variants) now run acceptably on consumer-class hardware with two RX6800 GPUs and 64 GB RAM. The author highlights Qwen3.6-27B (dense) for accuracy, Qwen3.6-35B-A3B (MoE) for speed, and Qwen-Coder-Next-80B (MoE) for coding tasks. Infrastructure improvements include llama.cpp's experimental router mode, ongoing work to persist attention checkpoints and context slots, and a personal fork that adds slot save/restore. The piece also compares harnesses (Hermes, Pi) and argues that capable local inference enables offline, libre-software experimentation without relying on commercial inference providers.
Alibaba’s Qwen 3.6 35B-A3B MoE Model and Local 24GB VRAM Guide
The article reviews Alibaba’s Qwen 3.6 35B‑A3B, a Mixture‑of‑Experts (MoE) LLM released April 16, 2026 under Apache 2.0, and explains why the model requires all 35B parameters to be resident in memory (creating a practical 24GB VRAM minimum). Benchmarks and quantization guidance show that on consumer 24GB GPUs the model can achieve high token throughput (e.g., ~120 tok/s on an RTX 4090 with Q4_K_M and tuned llama.cpp settings). The piece compares the MoE 35B-A3B to the dense Qwen 3.6 27B (which fits in ~16GB and scores higher on SWE‑bench), details VRAM usage by quantization and KV cache, and provides hardware and backend recommendations for local deployment (Ollama, llama.cpp, vLLM, Unsloth quant). Published on Dev.to (source runaihome.com republished) on 2026-06-11.
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