Deploying Llama 3 8B on Consumer Hardware

Start with memory pressure, not benchmark screenshots

An 8B model can look easy to run until context length, KV cache, batch size, and everything else on the machine start competing for the same memory. On consumer hardware, that pressure often shows up before raw compute becomes the interesting problem.

Size the context for the workload you actually have. Watch RAM, VRAM, and swap while requests are running, not just while the model is idle. If latency suddenly falls off a cliff, memory movement is one of the first places worth checking.

Stabilize one worker before you add orchestration

Get one model process to start predictably, accept a bounded number of requests, and shut down cleanly. A thin API wrapper and a small queue are usually enough for a personal system or a small team.

Adding a scheduler, multiple workers, or an agent layer before the single-worker path is boring and predictable just creates more places to hide the same resource problem.

// Example runtime configuration for a local inference worker
function initializeModel(weightsPath: string) {
  return new LlamaModel({
    weightsPath,
    contextSize: 4096,
    gpuLayers: 32,
    batchSize: 512,
  });
}

Make failure modes visible

Track cold-start time, queue depth, request latency, memory pressure, and model load failures. Cap concurrency instead of letting a burst of requests turn the machine into a swap test.

Local inference is still a service. The fact that the hardware is under your desk does not remove the need for health checks, bounded work, or a clear fallback when the model cannot accept another request.