What's currently the 'smartest' LLM to use on 8GB vram and 16 RAM and same thing for 8 VRAM and 64 RAM?

Been trying to find something that actually handles my workload well instead of just being "fine." Started on Qwen 2.5 7B, moved to Qwen 3 8B, and right now I'm using Nemotron 3 Ultra (the big 550B one) through an API since obviously that's not running on my GPU lol. But I want a proper local setup too. My use case is kind of a mix — coding/reasoning stuff, some agentic/tool-calling pipelines where the model needs to chain steps reliably, and structured data extraction where I actually need clean output, not something that half-breaks the format every few runs. So a few things I'm trying to figure out: For 8GB VRAM / 16GB RAM — is there an actual best pick right now, or is it still just Qwen 3 8B vs Nemotron Nano/Super depending on the day? Feels like everyone has a different opinion. For 8GB VRAM but bumped up to 64GB system RAM — does that actually change much? I keep seeing people talk about offloading MoE models partially to RAM but I don't know if that's genuinely worth it or just adds latency for a small quality bump. Quantization-wise, what's actually the sweet spot here? Q4_K_M, Q5, Q6? I don't care as much about a few IQ points on chat quality, but for tool-calling/structured output I've noticed lower quants sometimes just... break the format, which is way more annoying than a slightly dumber answer. Also curious if anyone's actually run Nemotron Nano or Super locally for agent/tool-use stuff specifically, not just the usual coding benchmarks everyone posts. Not looking for benchmark charts, more interested in what people are actually running day to day on similar specs. submitted by /u/MysteriumP [link] [comments]

Source: r/ollama | 2026-08-01