RTX 4060 Ti 16GB

The 16GB variant’s relevant trade-off: about 288 GB/s of memory bandwidth, less than a third of the RTX 4090’s. For memory-bandwidth-bound LLM inference, this caps achievable tokens/sec well below what the VRAM capacity alone would suggest. A card with more VRAM than bandwidth is common in this tier, and it matters for throughput expectations.

Where 16GB actually helps

16GB fits an 8B model at Q8_0 (about 8.5 bits per weight, roughly 8 GB of weights for the Llama 3.1 8B file) with room for a large KV cache. By this site’s formula it needs about 13.1 GB at 32K context, so it fits, and about 17.5 GB at 64K, so it does not. A 13B model at Q4_K_M needs about 14.9 GB at 8K context by the generic estimate, so it fits with little room to spare, and longer context does not fit. This is the practical sweet spot: full GPU offload for 8B-class models, and for 13B at short context, without managing CPU/GPU layer splitting.

Bandwidth vs. capacity trade-off

Compare against the 4090: the 4090 has 1.5x the VRAM and about 3.5x the memory bandwidth (1,008 GB/s against 288 GB/s). For a model that fits in either card’s VRAM (for example an 8B model), the 4090 will produce meaningfully higher tokens/sec on the same quantization. VRAM headroom past what the model plus KV cache needs doesn’t translate to speed once the model already fits.

Checking the model loaded fully on the GPU in Ollama

After loading an 8B Q8_0 model, run ollama ps and look at the PROCESSOR column, which shows how the model is split between GPU and CPU. If part of it is on the CPU, shorten the context or use a smaller quantization. Options for forcing offload differ between Ollama builds and GPU vendors, so check the Ollama documentation for your version.

Where the 34 tokens/sec comes from

The figure is a theoretical ceiling, not a measurement. Generating each token reads the whole model from memory, so the maximum speed is roughly memory bandwidth divided by model size. The card’s memory bandwidth is about 288 GB/s and the Llama 3.1 8B Q8_0 file is about 8.5 GB (7.95 GiB), so 288 / 8.5 is about 34 tokens/sec. Real speeds are lower than the ceiling.

Known limitation

This is a calculation, not a benchmark, and it has not been measured on LocalNodeOps hardware, so the entry keeps the “estimated” label. The 288 GB/s bandwidth figure comes from published spec listings. See /methodology for what “estimated” means on this site.