Yes — absolutely. This is a well-established technique called **hybrid CPU-GPU inference** (a.k.a. *layer offloading* or *weight offloading*), and your 64 GB of system RAM is exactly what makes it powerful. The idea: load as many model layers as fit in your GPU's VRAM, and let the CPU + 64 GB RAM handle the rest. The GPU computes the layers it holds; the CPU computes the offloaded layers. Both work on the same inference pass — you're not running two separate models. ## Best tools for this (pick one) **1. llama.cpp — the standard, most flexible** (GGUF models) ```bash ./main -m model.Q4_K_M.gguf -ngl 35 # 35 layers to GPU, rest to CPU+RAM # or -ngl 999 to offload as much as fits, automatically leaving the rest on CPU ``` `-ngl` (n_gpu_layers) is the key knob: raise it until you hit ~80–90% VRAM usage, then leave the rest on CPU. Works with NVIDIA (CUDA), AMD (HIP/Vulkan), and Intel (SYCL). Also powers most GUI tools below. **2. Ollama — easiest** (built on llama.cpp, does this automatically) ```bash ollama run llama3.1:8b --num-gpu-layers 40 # or set OLLAMA_GPU_LAYERS=40 (or OLLAMA_MAX_LOADED_MODELS) as an env var ``` When a model exceeds your VRAM, Ollama automatically spills the overflow into your 64 GB RAM. **3. vLLM — for serving many requests** (production-grade) ```bash vllm serve meta-llama/Llama-3.3-70B-Instruct --cpu-offload-gb 48 --max-model-len 8192 ``` `--cpu-offload-gb` sets the max RAM to use for weights that don't fit in VRAM — it fills VRAM first, then uses only as much RAM as needed. Note: weights stream across PCIe each token, so throughput drops. **4. KTransformers** — best performance-per-layer for MoE models (Miqutral, Qwen, DeepSeek) since it keeps only *active experts* on the GPU and uses RAM for the rest, with specialized CPU kernels. Often beats llama.cpp speed on hybrid setups. **5. One-click GUI wrappers:** LM Studio, KoboldCpp, GPT4All, Jan — all wrap llama.cpp and let you drag a slider for GPU offload. ## What you can actually run with 64 GB RAM - **70B-class models (Llama 3 70B, Qwen 72B) at Q4 quantization (~40 GB)** — fits entirely in your RAM even with a small GPU; offload whatever layers your VRAM holds for a big speedup. - **100B+ dense models (Q4, ~60 GB)** — tight but doable. - **MoE models (e.g., Mixtral 8x7B, Qwen2.5-32B-MoE)** — these shine in hybrid mode because only a fraction of parameters are active per token. - With just 8–16 GB VRAM + 64 GB RAM, a 70B Q4 model is very feasible. ## Key caveats & tuning tips - **The PCIe bottleneck is real:** data shuttles between RAM and VRAM every token, so hybrid is always slower than full-GPU. PCIe 4.0 x16 helps a lot. - **CPU memory bandwidth is king** for the CPU side: DDR5 dual/quad-channel + high core count makes hybrid much faster. Check your RAM speed — this often matters more than core count. - **Quantize:** Q4_K_M / Q5_K_M hit the sweet spot of size vs. quality. - **Find the "knee":** benchmark tokens/sec at `-ngl 0, 10, 20, 30...` — the first layers on GPU give the biggest gains; the curve flattens after that. Aim to fill VRAM to ~80–90% (watch with `nvtop` or Task Manager). - **llama.cpp tip:** set a batch size of 512 (`-b 512`) so prompt processing uses CPU threads efficiently. - For interactive use, expect **5–15 tokens/s** on a good hybrid 70B setup; for MoE models it can be much faster. **Bottom line:** yes — with 64 GB RAM, hybrid inference effectively turns your RAM into "extra VRAM." It won't match full-GPU speed, but it lets you run models 2–5× larger than your VRAM alone, which is exactly what tools like llama.cpp, Ollama, vLLM, and KTransformers are built for.