Table of Contents

How Fast Is Local Inference Compared to Cloud APIs?


TL;DR

Local inference with LM-Kit.NET eliminates network latency entirely, delivering instant time-to-first-token for small models. GPU-accelerated inference on a modern NVIDIA GPU runs 5x to 15x faster than CPU for models in the 4B to 8B range. Compared to cloud APIs, local inference trades raw throughput on very large models for zero network overhead, no rate limits, and predictable latency under your full control.


What Affects Inference Speed

Four factors determine how fast token generation runs:

Factor Impact
Model size Larger models require more computation per token. A 1B model generates tokens much faster than a 27B model on the same hardware.
GPU backend CUDA and Metal provide the fastest inference. Vulkan is slightly slower but works across GPU vendors. CPU is the slowest for models above 3B.
Quantization All catalog models use Q4_K_M (4-bit) quantization, which is optimized for speed on consumer hardware. Lower-bit quantization is faster but may reduce quality.
Context length Longer conversations or larger document contexts slow down generation because the attention mechanism scales with token count.

CPU vs GPU Performance by Model Size

Model Size CPU (AVX2) GPU (CUDA/Metal) Recommendation
Under 1B Fast. Suitable for real-time interactive use. Minimal improvement over CPU. CPU is fine.
1B to 3B Responsive for most tasks. Noticeable speed-up. CPU works well. GPU is a nice-to-have.
4B to 8B Usable, but noticeably slower. 5x to 15x faster token generation. GPU strongly recommended.
12B and above Slow. Multi-second response times. Essential for interactive use. GPU effectively required.

A model that takes several seconds per response on CPU can generate tokens in milliseconds when offloaded to a supported GPU.


Local vs Cloud: Latency Breakdown

A cloud API call involves multiple latency components that local inference eliminates:

Component Cloud API Local Inference
Network round trip 50 to 300+ ms depending on region 0 ms
Queue wait time Variable. Can spike during peak usage. 0 ms (your hardware, your queue)
Time-to-first-token 200 to 2000+ ms Typically under 100 ms for models up to 8B on GPU
Rate limits Per-minute and per-day caps None
Throughput consistency Variable. Shared infrastructure. Deterministic. Depends only on your hardware.

Where local wins: Low-latency applications (chatbots, real-time agents, interactive tools), air-gapped environments, cost-sensitive high-volume workloads, and privacy-critical use cases.

Where cloud wins: Very large models (70B+) that exceed local GPU memory, burst workloads that exceed your hardware capacity, and scenarios where you need the absolute highest quality output from frontier models.


Measuring Performance on Your Hardware

LM-Kit.NET provides a built-in performance scoring API to evaluate your hardware without running a full benchmark:

using LMKit.Model;

// Quick heuristic score (0.0 to 1.0) without loading the model
double score = LM.GetPerformanceScore(modelUri);
Console.WriteLine($"Performance score: {score:F2}");

For precise measurements, use the MemoryEstimation.FitParameters() API to determine optimal context size and GPU layer count, then measure actual token generation speed in your application:

using LMKit.Model;
using LMKit.TextGeneration;
using System.Diagnostics;

using LM model = LM.LoadFromModelID("qwen3.5:9b");
var chat = new MultiTurnConversation(model);

var sw = Stopwatch.StartNew();
var result = chat.Submit("Explain the observer pattern in software design.");
sw.Stop();

Console.WriteLine($"Response length: {result.Length} characters");
Console.WriteLine($"Total time: {sw.ElapsedMilliseconds} ms");

Tips for Maximizing Speed

  • Use GPU inference for any model above 3B parameters. The speed difference is dramatic.
  • Pick AVX2 over SSE4 on CPU. The SDK selects this automatically on supported processors.
  • Reduce context size if you do not need long conversations. Shorter context means faster attention computation.
  • Offload all layers to GPU when VRAM allows. Partial offloading (some layers on CPU, some on GPU) is slower than full GPU offloading.
  • Choose the right model size for your task. An 8B model that runs fast on GPU often delivers better user experience than a 27B model that runs slowly.

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