Know what your hardware can actually run.
LocalNodeOps tracks real inference benchmarks — tokens per second, VRAM usage, and quantization trade-offs — across consumer and prosumer hardware, so you can pick a setup before you buy it.
Hardware benchmarks
Tokens-per-second and memory bandwidth figures measured on real consumer hardware, tagged verified or community-reported.
RTX 4090 24GB
24GB VRAM handles 70B models at Q4 quantization with room for an 8K context window.
View benchmarkRTX 4060 Ti 16GB
Best price-to-VRAM ratio for 8B-13B models at full precision quantization.
View benchmarkRyzen 9 7950X
Usable CPU-only fallback for 7B models when GPU offload isn't available.
View benchmarkDDR5-6000 Dual Channel
Minimum viable bandwidth for hybrid CPU/GPU offload setups without heavy bottlenecking.
View benchmarkGGUF Q4_K_M
The sweet spot for most consumer GPUs — minimal quality loss, roughly 4x size reduction.
View benchmarkAWQ 4-bit
Faster inference than GGUF at similar bit-width, at a small extra quality cost.
View benchmarkLatest from the blog
Guides on quantization, VRAM budgeting, and diagnosing common local inference errors.
The Baseline Architecture for Local LLM Orchestration: Avoiding VRAM Bottlenecks
What actually consumes VRAM in local LLM serving, and how to architect a baseline setup that doesn't blow past it.
How to budget VRAM for a 70B model
A walkthrough of how quantization level, context length, and batch size trade off against the VRAM you actually have.
Ollama vs llama.cpp server: when to use which
Ollama trades control for convenience. Here's where that trade stops being worth it.