98GB Model Gets 3x Speedup on RTX 4060 Ti
Chuyito · reddit · 2026-07-16
On a budget rig with an RTX 4060 Ti 16GB and a 6-core CPU, the author used llama.cpp to run a 98GB quantized DeepSeek-V4-Flash model, boosting throughput from 2 tok/s to 7 tok/s in just the past week.
The post shares hardware specs, test logs, and config snippets, emphasizing that such "CPU generation" is becoming genuinely viable. Even though the model's VRAM requirements far exceed the local GPU capacity, speeds have improved dramatically through CPU-assisted generation and continuous runtime optimizations. The author attributes this to recent ongoing optimizations in llama.cpp commits, making it a must-watch for local LLM inference progress.
More from Infra
- oMLX 0.5.2 adds Mac menu-bar stats, low-bit decode kernels, and faster downloads — awnihannun · 2026-07-22
- Strangeworks launches Aura to turn enterprise ops into production optimization systems — whurley · 2026-07-22
- Graph workload 854.graph500 enters SPEC CPU 2026 as a new CPU benchmark — Prof_DavidBader · 2026-07-22
- Hybrid and local inference are emerging as a response to AI energy and token costs — dmitry140 · 2026-07-22
- NVIDIA details Vera CPU with 2x performance claims and a 22,000-core rack — ryanshrout · 2026-07-22
- NVIDIA says Vera Rubin NVL72 delivers 10x more tokens per megawatt than Blackwell — nvidia · 2026-07-22