Salka Elmadani
ec36668cf5
Better output from the same model. Fused computation, adaptive precision, surgical expert loading. 305 KB, 19 backends, zero dependencies. https://inference-x.com
198 lines
8.2 KiB
Markdown
198 lines
8.2 KiB
Markdown
# How Inference-X Works
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> *A model's intelligence is in its weights. Everything between the weights and your screen is overhead. We removed the overhead.*
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---
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## The problem nobody talks about
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When you download a 7B model from Hugging Face, you're getting an artifact that was trained for months on thousands of GPUs in FP32 precision. That model — the original, the teacher's intent — produces a certain quality of output.
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But you never see that output.
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What you see is the model's intelligence *after* it's been pushed through an inference engine. And every engine adds noise:
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```
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Original model (FP32)
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→ Quantized to Q4_K (75% of data removed — intentional, necessary)
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→ Loaded into framework (PyTorch, llama.cpp, vLLM: 10-500 MB of overhead)
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→ Dequantized to intermediate buffer (rounding errors introduced)
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→ Matrix multiply (separate pass — more rounding)
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→ All experts loaded in memory (97% unused, competing for cache)
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→ Uniform precision across all layers (simple queries processed like complex ones)
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→ Output
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What you get is the model's signal + accumulated noise from every step.
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```
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This is how every inference engine works. The model is the same. The output varies depending on how much noise the engine adds.
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---
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## What Inference-X does differently
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We don't add features. We remove steps.
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### 1. Fused computation — zero intermediate buffers
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Standard: dequantize block → store in FP32 buffer → matrix multiply against buffer.
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Two passes. One temporary allocation. Rounding errors at each boundary.
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Inference-X: dequantize and multiply *in the same instruction loop*. One pass. No buffer. The quantized value goes directly from the block structure to the accumulator in a single fused operation.
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```c++
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// Standard: two passes, one buffer
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float buffer[K];
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dequant_q4k(buffer, weights, K); // pass 1: dequant → buffer (rounding)
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float result = dot(buffer, input, K); // pass 2: buffer × input (rounding)
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// Inference-X: one pass, no buffer
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float result = fused_dot_q4k(weights, input, K); // dequant + dot in one loop
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```
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Fewer floating-point operations = fewer rounding errors = output closer to the theoretical FP32 result.
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This is implemented for 10 quantization formats with hand-tuned AVX2/AVX-512 SIMD kernels.
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### 2. Adaptive precision — the model breathes
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Not every question is hard. "What's 2+2?" doesn't need the same computational depth as "Explain quantum entanglement in terms a physicist would appreciate."
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Inference-X analyzes each query *before* inference begins using Shannon entropy and vocabulary diversity. The result is a complexity score that determines how much precision each layer gets:
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| Query complexity | Early layers | Middle layers | Late layers | RAM savings |
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|---|---|---|---|---|
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| Simple (H < 0.3) | Q2_K | Q4_K | Base | ~26% |
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| Moderate (0.3–0.6) | Q4_K | Base | Base | ~10% |
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| Complex (H > 0.6) | Base | Base | Base | 0% |
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Simple queries get faster answers with no quality loss — because the extra precision wasn't contributing signal, only noise. Complex queries get full precision where it matters.
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The model file doesn't change. The binary doesn't change. The depth adapts to the question.
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### 3. Surgical expert loading — silence the irrelevant
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Mixture-of-Experts models (DeepSeek, Kimi K2.5) have 256–384 experts per layer but activate only 8 per token. Standard engines load all experts into RAM and let the OS manage caching.
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Inference-X does something different: it tells the OS exactly which experts are needed (predictive prefetch via `madvise(WILLNEED)`) and which are not (`madvise(DONTNEED)`). Inactive experts are surgically evicted from memory.
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Result: 48× I/O reduction. But more importantly — the inactive experts don't compete with active ones for CPU cache. The signal path is clean.
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This is how a 226 GB model (Kimi K2.5, 1 trillion parameters) runs on a machine with 17 GB of RAM. Not by being clever about loading. By being precise about *unloading*.
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### 4. Direct quantization support — 23 formats, native
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Every quantization format has a different way of packing information into fewer bits. Most engines support a handful and convert the rest.
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Inference-X has native dequantization and fused dot products for 23 formats — from Q2_K (2 bits) to FP32 (32 bits). No conversion step. No intermediate format. The engine speaks the model's native dialect.
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| Format family | Variants | Block size | Bits/weight |
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|---|---|---|---|
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| K-quant | Q2_K, Q3_K, Q4_K, Q5_K, Q6_K | 256 | 2.6–6.6 |
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| Standard | Q4_0, Q4_1, Q5_0, Q5_1, Q8_0, Q8_1 | 32 | 4.5–9.0 |
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| IQ (importance) | IQ1, IQ2, IQ3, IQ4 | varies | 1.0–4.5 |
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| Float | F16, BF16, F32 | 1 | 16–32 |
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### 5. 305 KB — the engine is invisible
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A framework has opinions. It has abstractions. It has object hierarchies. Every layer of abstraction is a layer of interpretation between the model and the hardware.
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Inference-X is 305 KB compiled. Header-only C++. No runtime. No garbage collector. No memory allocator. The binary is so small that the entire engine fits in L2 cache of a modern CPU.
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The engine should be invisible. You should hear the model, not the engine. 305 KB is the engineering of disappearance.
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---
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## What this means in practice
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### Better output quality at the same model size
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Because the computation path is cleaner, the same Q4_K model produces output that is closer to its FP32 theoretical maximum through Inference-X than through engines with intermediate buffers and uniform precision.
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This isn't a benchmark number. It's a property of the mathematics: fewer rounding operations = less accumulated error = higher fidelity to the original training.
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### Same output quality at a smaller model size
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If a 7B model through Inference-X produces output quality comparable to a 7B through a standard engine, then a smaller model through Inference-X may match a larger model through a standard engine.
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Less RAM. Less storage. Faster inference. Same answers.
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### Runs on hardware that "shouldn't" work
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Kimi K2.5 (1T parameters, 226 GB) runs on 17 GB of RAM. Not in theory — in production. Because surgical expert management reduces the active memory footprint to what actually contributes to each token.
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7B models run on Raspberry Pi. 3B models run on ESP32. The engine's minimal footprint leaves almost all system resources for the model.
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---
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## The business case
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```
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Standard deployment:
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Model: Llama-3.1-70B-Q4_K
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Hardware: 128 GB server, 2× A100
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Cost: ~$40,000/year (cloud)
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Output quality: baseline
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Inference-X deployment:
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Model: Llama-3.1-70B-Q4_K (same model)
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Hardware: 128 GB server, CPU-only
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Cost: ~$2,400/year (Hetzner EPYC)
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Output quality: equal or better (cleaner computation path)
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Savings: 94%
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Quality: maintained or improved
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```
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The savings come from two places:
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1. No GPU required (the engine runs efficiently on CPU with SIMD optimization)
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2. Adaptive precision reduces effective memory bandwidth by 10-26% for typical workloads
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For MoE models, the advantage is larger:
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```
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Kimi K2.5 on GPU cluster:
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8× H100, NVLink, ~$200,000/year
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All 384 experts loaded, 376 idle per token
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Kimi K2.5 on Inference-X:
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256 GB EPYC server, ~$4,800/year
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8 active experts loaded, 376 surgically evicted
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Same model. Same answers. 97.6% cost reduction.
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```
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---
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## Who this is for
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**Edge deployments** — Run models on devices without cloud connectivity. The 305 KB binary deploys to ARM, RISC-V, FPGA, microcontrollers.
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**Cost-sensitive inference** — Replace GPU clusters with CPU servers for the same or better quality. Pay for RAM, not for CUDA cores.
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**Hardware manufacturers** — Integrate Inference-X as the inference layer for custom silicon. One integration covers every model format.
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**Sovereign AI** — Run national-language models on national infrastructure. No data leaves the country. No dependency on foreign API providers.
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**Research** — Test models across 19 hardware targets from a single binary. Compare performance across architectures without rewriting code.
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---
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## Try it
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```bash
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git clone https://github.com/ElmadaniS/inference-x
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cd inference-x
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make
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./inference-x model.gguf -p "Hello"
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```
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One binary. One command. The model speaks directly.
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---
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*The best inference engine is the one you don't notice.*
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*You should hear the model, not the framework.*
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◆
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