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IQ3_S vs IQ2 Tiers: GGUF Quantization for 16-GB Laptop Stacks (2026)

GGUF quantization for 16-GB laptops in 2026: IQ3_S (11.8 GB, 99.8% of base quality) vs IQ2_S and IQ2_XS for KV headroom, 12-GB cards and batch jobs — with measured numbers.

Reviewed by Michael Kerkhoff, as of

Definition
IST-DASLab's GSQ+RCO releases turn quantization into a decision with measured numbers: Qwen3.8-27B ships as GGUF in four tiers from 8.4 to 11.8 GB, and the recommended IQ3_S keeps 99.8% of the BF16 base task average at a 4.6× reduction — fully inside a mid-range 16-GB GPU. Under the old uniform-quantization logic a 27B dense model needed 18–20 GB; per-tensor search pushed it under the edge. The real question on a laptop is which file to download: the largest tier that reproduces base quality (AIME25 and LiveCodeBench exact, GPQA-Diamond off by 0.51 points), or a smaller IQ2 tier that buys KV-cache headroom and batch capacity. This comparison weighs quality, headroom, hardware fit, throughput and setup — and ends with the decision rule.
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IQ3_S (3.5 bpw, 11.8 GB) — the recommended quality tierIQ2 tiers (2.75/2.5 bpw, 9.3/8.4 GB) — the headroom tiers

Detailed Comparison

A side-by-side analysis of key factors to help you make the right choice.

IQ3_S (3.5 bpw, 11.8 GB) — the recommended quality tier vs IQ2 tiers (2.75/2.5 bpw, 9.3/8.4 GB) — the headroom tiers
FactorIQ3_S (3.5 bpw, 11.8 GB) — the recommended quality tierIQ2 tiers (2.75/2.5 bpw, 9.3/8.4 GB) — the headroom tiers
Task quality at full contextAIME25 (100.00) and LiveCodeBench v6 (85.71) reproduced exactly; GPQA-Diamond off by 0.51 points — a 99.8% task average at 3.5 bpw. WinnerIQ2_S reaches base level on AIME25 but trails on GPQA-Diamond (86.36 vs 89.39); IQ2_XS is lowest at 84.85 with 2.5 bpw.
VRAM headroom for context and KV cacheOn a 16-GB card the 11.8 GB file leaves 3–4 GB for KV cache and activations — contexts up to ~100k are practically usable.6.7–7.6 GB free on 16 GB: more window for 112k contexts and larger parallel batches. Winner
Fit on older and smaller cardsNeeds the full 16-GB class; on a 12-GB RX 6800M it nearly fills the card (97% at 4K context).IQ2_XS leaves ~1.3 GB of headroom even on 12-GB laptop GPUs — the only tier that fits there comfortably. Winner
Interactive and batch throughputSame per-token speed: ~40 tok/s on an RTX 5060 Ti (552 tok/s prefill at 112k), ~136 tok/s on a 5090; streamed text stays above reading speed.Identical decode speed, but the smaller footprint allows bigger batch sizes — the lever that matters for extraction and classification. Winner
Setup and workflowPlain GGUF for llama.cpp, Ollama, LM Studio; optional -mtp build (+0.35 GB) adds speculative decoding with identical weights. WinnerSame files, same tools, same 0.9 GB mmproj for vision — zero migration cost either way.
Total Score · 0 ties2 / 53 / 5

Key Statistics

Real data from verified industry sources to support your decision.

All statistics come from verified third-party sources. Source, year, and direct link are shown on each metric.

When to Choose Each Option

Clear guidance based on your specific situation and needs.

Our Recommendation

There is no universal winner — the axis is quality-exactness versus headroom. IQ3_S is the rational default for the 16-GB point of 2026: AIME25 and LiveCodeBench reproduced exactly, GPQA-Diamond off by 0.51 points, and ~40 tok/s on an RTX 5060 Ti keeps the model above reading speed even at long context. The IQ2 tiers are different instruments, not worse ones: on a 12-GB card IQ2_XS is the only tier that fits with real headroom, and for batch-style work the smaller files buy more KV space and larger batches for a 3–5 point GPQA gap that rarely surfaces in short, format-like tasks. The decision rule: interactive and graded work on 16 GB → IQ3_S; 12-GB card or pure batch throughput → IQ2_S or IQ2_XS. Dense compression has a hard floor near 2.5 bpw, so these files already cover the useful range — and the .rco-allocation.txt files make every tier's budget verifiable.

Choose IQ3_S (3.5 bpw, 11.8 GB) — the recommended quality tier when...
  • You work interactively on a 16-GB card — chat, coding assistants, agent loops — and want base-level reasoning with nothing measurable lost.
  • You need ~100k context while keeping full task quality; the 3–4 GB left by the 11.8 GB file covers your window.
  • Your tasks are graded or reasoning-heavy (GPQA-style Q&A, multi-step code), where the 3–5 point gap of the IQ2 tiers actually shows up.
  • You can afford the MTP variant (+0.35 GB) and want speculative decoding on top of the quality tier.
Choose IQ2 tiers (2.75/2.5 bpw, 9.3/8.4 GB) — the headroom tiers when...
  • You run on a 12-GB laptop GPU — IQ2_XS at 8.4 GB is the only tier that fits with ~1.3 GB of headroom.
  • You process batch jobs (classification, extraction, many documents) where 3–5 GPQA points rarely matter and larger batch sizes dominate throughput.
  • You want maximum KV-cache space for very long windows or several concurrent sessions on one card.
  • You are prototyping and value fast load/teardown cycles over the last half percent of quality.

Common questions about this comparison answered.

Frequently Asked Questions

(01)Which GGUF file do I take on a 16-GB card?
IQ3_S at 11.8 GB if you need full model quality and work up to ~100k–112k context. IQ2_S at 9.3 GB if you want more headroom for large batches or long windows — it already reaches base level on AIME25. IQ2_XS is the choice for 12-GB laptops.
(02)Is 40 tok/s on the 5060 Ti a fixed value?
No — the rate depends on context: up to ~50 tok/s at the start of a window, ~25–30 tok/s toward the end of a filled 112k context. Prefill stays at 552 tok/s because it runs in parallel over prompt tokens. Plan a range for agent latency, not a single number.
(03)Do I need the -mtp variant?
If your harness supports speculative decoding: yes. The MTP head costs only 0.35 GB, speeds up decoding measurably, and keeps quality identical because the base tensor allocation is unchanged.
(04)How do these tiers beat older dynamic quantizations?
GSQ+RCO optimizes per tensor instead of per layer group: at 8.4 GB, IQ2_XS leads the same-size UD-IQ2_S by 10 points on AIME25; at ~12 GB, IQ3_S leads by 3.33 points with 0.2 GB less size. All files remain standard GGUF.

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