CPU-Only Inference Too Slow? Add a GPU to 10x Your Token Speed

Running a 70B language model on CPU alone feels like watching paint dry, with output trickling out at one to three tokens per second while the cores sit pegged. The fix is rarely a faster CPU. Adding a GPU is the single biggest jump you can make for local inference, because the model's weights move into fast video memory and the maths runs on thousands of parallel cores instead of a handful of CPU threads. Even a partial offload changes the experience from unusable to genuinely workable.

Quick Answer

A GPU is the most impactful upgrade for slow local inference. CPU-only on a 70B model sits around one to three tokens per second; partial GPU offload lifts it to roughly eight to twelve, and a card that holds the whole quantised model in VRAM can reach 40 tokens per second or more. Unified-memory machines skip the PCIe bottleneck entirely by sharing one fast memory pool.

Why the CPU chokes and the GPU does not

Large-model inference is bottlenecked by memory bandwidth, not raw compute. A CPU reads weights from system RAM over a relatively narrow bus, so every token forces it to stream gigabytes through a slow pipe. A GPU pairs thousands of cores with very high-bandwidth memory sitting right next to them, which is exactly the shape of work that token generation demands. That is why moving even part of the model onto a graphics card produces a step change rather than a modest bump.

The catch is fit. If the model is larger than your VRAM, the system spills the overflow back to system RAM and the GPU keeps reaching across the PCIe bus to fetch it. That spill is where the heavy penalty lives, with partial offload often running five to ten times slower than a model that sits entirely in VRAM. The goal is to get as much of the model resident on the card as your memory allows.

Picking the right path for your budget

For most people the practical win is a card with enough VRAM to hold a quantised version of the model you actually use. Smaller 7B and 13B models fit comfortably on mainstream cards and fly. A 70B model is far more demanding, and as of mid-2026 it takes a top-tier consumer GPU with large VRAM to hold a quantised copy in memory and hit the high-40s tokens per second. Short of that, you accept partial offload and the eight-to-twelve range, which is still a transformation over CPU-only.

Unified-memory machines are the other route. By sharing one large, fast memory pool between processor and graphics, they can load a 70B model without splitting it across a slow bus at all, trading peak speed for the ability to run big models cleanly. Evetech's AI-ready PC range covers both styles, from high-VRAM GPU builds to unified-memory boxes, and the best-selling PC desktops list is a quick gauge of what local-AI users are actually buying right now.

Frequently Asked Questions

How much faster is a GPU than CPU-only for a 70B model?

Expect a jump from roughly one to three tokens per second on CPU to around eight to twelve with partial GPU offload, and 40 or more if the whole quantised model fits in VRAM. The exact figures depend on quantisation and the card.

Will more system RAM fix slow inference?

Not really. Adding RAM lets you load bigger models without crashing, but it does nothing for speed, since system RAM bandwidth is the bottleneck the GPU exists to bypass. Spend on VRAM or a faster compute path instead.

What is partial offload?

It means putting as many model layers as fit onto the GPU and leaving the rest in system RAM. It is much faster than CPU-only but slower than a fully resident model, because the card keeps fetching the leftover layers across the PCIe bus.

Is unified memory better than a dedicated GPU?

For very large models it can be, because one big shared memory pool avoids splitting the model across a slow bus. A dedicated high-VRAM GPU usually wins on peak speed for models that fit, so the right choice depends on the model size you run.