More industry teams want LLM inference on consumer GPUs or edge devices—for cost, latency, and data residency. Public demos that “run once” often diverge from stacks that stay stable under real memory pressure. The gap is usually engineering clarity on VRAM, decode, and the device memory model—not another generic gateway.
Challenge one: memory budgets. Weights, activations, KV cache, and scratch buffers stack; concurrency often surfaces OOM before throughput collapses. Consumer and edge boxes are already tight, and must leave headroom for OS, speech, containers, or other services. Engines that do not account for every byte before run-time tend to thrash in production.
Challenge two: MoE and decode paths. Mixture-of-experts amplifies expert switching and kernel fragmentation; token-by-token decode often sits in inefficient batch=1 regimes where generic GEMM is not always best. KV traffic and bandwidth pressure become visible with long context and multi-request load. Structure, kernels, and scheduling must move together.
Challenge three: edge unified memory. On Jetson-class devices, CPU and GPU share a limited pool while STT/TTS, agents, and inference compete. Desktop-oriented stacks may not model that constraint. Without MemoryBudget-style accounting, OOM guards, and prefix-KV reuse, “it runs” becomes “it dies when busy.”
A useful problem statement is therefore an acceptance brief: target hardware and co-resident processes, model and quant format, decode vs prefill weight, concurrency and soak protocol. Any public multipliers are illustrative only (subject to retest)—re-measure in the customer environment.
Mr F GPU Runtime work is organized around these bottlenecks. Product narrative and trial flow: #/mrf (trial); tech solutions catalog: #/exclusive.
Background note only—not a performance promise for any vendor or open-source project; not investment or legal advice.