Choose between 4-bit, 8-bit and bf16 LLM inference

What changes when model weights use fewer bits

Quantization represents model weights with fewer bits. That reduces the space used by those weights and changes their numerical representation. The total runtime memory still includes other allocations, such as context state and temporary work. MLX LM supports model conversion and quantized inference on Apple Silicon. MLX LM documentation

A 4-bit label does not specify total serving memory. Check the quantization method, model architecture, runtime and context as well. The Apple Silicon memory guide explains the difference between a raw weight estimate, download size and usable serving capacity.

What research establishes

Dettmers and Zettlemoyer's ICML 2023 study compared the relationship between model size, weight precision and zero-shot accuracy across several model families. Its results support evaluating 4-bit models when memory is constrained. The experiments covered BLOOM, OPT, NeoX/Pythia and GPT-2; they do not establish the quality or speed of the Qwen3.8 variants in the PROXIES.SX catalog. The case for 4-bit precision

Do not turn a general research finding into a fixed percentage of quality loss for your application. A model can pass a broad test and still fail a particular extraction format, language or coding task.

A 2025 profiling study by Benazir and Lin tested several Apple Silicon and NVIDIA configurations and examined dequantization overhead alongside memory and compute limits. It reports that lower weight precision does not consistently produce faster inference across hardware. Its configurations do not establish performance for this rental catalog. Apple Silicon quantization profiling

What the compute catalog establishes

The saved September 11 catalog lists three Qwen3.8 27B configurations. Its 4-bit entry requires 24 GB and publishes a 16,384 context limit; 8-bit requires 48 GB and bf16 requires 96 GB, both with 32,768 context. These are service fields, not measured comparisons. Exact repository names and download sizes are in the model catalog guide.

The context limits belong to these published configurations; 4-bit quantization does not inherently halve context capacity. BF16 stores values in a 16-bit floating-point format. Its catalog label describes the weights, not the precision of every runtime calculation.

Separate weight precision from context settings

MLX LM documents prompt caching, a rotating KV cache and configurable prefill steps. These affect how the runtime handles context and memory. They are separate from converting model weights to a lower precision. MLX LM context features

A managed rental does not give you direct control over every runtime option. Compare the configuration the endpoint actually exposes. Keep model revision, input text, output cap and generation settings constant when evaluating precision; record an unavailable setting as unknown.

Suppose an extraction test fails an exact number. Save that case and check whether the same failure occurs with each precision before attributing it to quantization. A prompt-format error, missing evidence or tokenizer difference can affect the result as well. For RAG, use the same retrieved passages in each run.

Choose the lowest-cost configuration that passes your task and capacity requirements. That is an application decision from your measurements, not a universal ranking of 4-bit, 8-bit and bf16 models. Use cost per accepted result when failed outputs require retries or review.

Compare quality before throughput

Build a small saved evaluation set from your real tasks. Include exact-answer cases, long inputs and cases where the correct behavior is to say the available information is insufficient. Establish the pass condition before seeing model outputs.

Keep the base model, prompt set, chat template and sampling settings as comparable as the service allows. Record any field you cannot inspect. Compare the same context length first; otherwise a change in retrieved evidence can be mistaken for a quantization effect.

For a RAG workload, test retrieval separately. More precise model weights cannot recover a document your retriever never supplied.

Measure latency on the actual configuration

Run timing tests only after defining acceptable task quality. Record first-token latency, completion time, errors and concurrency with the benchmark worksheet. A shorter weight representation does not prove a particular speedup on a given Mac and runtime.

If the 4-bit and 8-bit endpoints run on different chips, describe the result as a comparison of those complete configurations. It cannot isolate the effect of quantization. Keep output lengths comparable so a shorter answer does not appear faster simply because it contains less work.

Choose the smallest configuration that passes

Start with a configuration whose documented memory and context limits fit the task. If it passes your quality and latency requirements, a larger allocation needs a concrete benefit to justify its rental cost. If it fails, inspect the failed examples before assuming precision is the cause.

A different prompt, better retrieval or a different base model may address the failure. Those options require their own test; the current managed service is limited to its published catalog and assigned model. Check current machine listings before scheduling any comparison. No rented endpoint or quantization benchmark was tested for this guide.