What is quantization in LLM deployment?
Quantization is the process of reducing the numeric precision of a model's weights, typically from 32-bit or 16-bit floats down to 8-bit or 4-bit integers. This shrinks the model's memory footprint by 2x to 8x and speeds up inference, usually at the cost of a small, measurable drop in output quality. For most private LLM deployments on SMB-grade hardware, a 4-bit or 8-bit quantized model is the practical default.
Why quantization matters for on-premise LLM deployment
Running a large language model on your own hardware is the only way to keep sensitive data fully private. The problem is that full-precision models are enormous. Llama 3.1 70B at 16-bit precision requires roughly 140 GB of VRAM, which means multiple enterprise-grade GPUs and a five-figure hardware bill before you've written a single line of application code.
Most SMBs don't have that infrastructure, and they shouldn't need it. Quantization is the practical solution that makes private deployment viable on a realistic budget. It's not a workaround or a compromise of last resort. It's the standard approach used in production by most teams shipping private LLMs today.
How quantization actually works
Every weight in a neural network is stored as a number. Full-precision training uses 32-bit floats (FP32). Most modern LLMs ship in 16-bit (FP16 or BF16). Quantization maps those high-precision numbers to a smaller set of values, most commonly 8-bit integers (INT8) or 4-bit integers (INT4). The math is lossy by definition, but the loss is often smaller than people expect.
In practice, an INT4-quantized Llama 3.1 70B model fits in roughly 35-40 GB of VRAM and runs on two consumer-grade A100s or a single H100. Benchmark scores drop by 2-5% on most reasoning tasks compared to the FP16 version. For a customer-service agent, a document Q&A system, or a clinical intake assistant, that difference is rarely noticeable to end users.
The two most common quantization formats we use in deployment are GGUF (via llama.cpp, good for CPU-heavy or mixed CPU/GPU setups) and GPTQ or AWQ (optimized for GPU inference). The right choice depends on your hardware profile, not a general preference.
When quantization isn't the right call
If you're running a smaller model already, like Llama 3.1 8B or Mistral 7B, the FP16 version may fit comfortably in a single GPU with VRAM to spare. Quantizing a model that already fits isn't wrong, but the tradeoff matters more at that scale since the quality loss is proportionally larger on smaller models.
For tasks where precision genuinely matters, such as complex multi-step financial calculations or clinical decision support where a subtle reasoning error has real consequences, we'll test quantized and full-precision versions against the same benchmark set before committing. In a small number of cases, the quality gap is wide enough that the hardware cost of running FP16 is justified. We've seen this in roughly one in ten healthcare deployments we've scoped.
How we handle quantization in private deployments
Every private LLM deployment we build starts with a hardware assessment. We pick the model size and quantization level together, treating them as one decision rather than two. For most SMB deployments in healthcare, finance, and logistics, we default to INT4 or INT8 quantized weights using GGUF or AWQ depending on the server setup, then run a targeted accuracy benchmark against the client's actual use cases before go-live.
We don't use quantization to cut corners on hardware costs for our margin. We use it because it's the honest path to a private, HIPAA-compliant system that a mid-size clinic or regional logistics company can actually afford to run. If the quality drop at INT4 is unacceptable for a specific workflow, we say so and spec the hardware needed for INT8 or FP16 instead.
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