--- type: "Comparison" title: "IQ3_S vs IQ2 Tiers: GGUF Quantization for 16-GB Laptop Stacks (2026)" description: "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." resource: "https://www.contextstudios.ai/comparisons/gguf-quantization-iq3-s-vs-iq2-tiers" language: "en" generated: by: "process:contextstudios-md/1" at: "2026-10-08T20:46:43.282Z" status: "stable" --- # IQ3_S vs IQ2 Tiers: GGUF Quantization for 16-GB Laptop Stacks (2026) 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. ## Detailed Comparison | Factor | IQ3_S (3.5 bpw, 11.8 GB) — the recommended quality tier | IQ2 tiers (2.75/2.5 bpw, 9.3/8.4 GB) — the headroom tiers | Winner | |--------|------|------|--------| | Task quality at full context | AIME25 (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. | IQ2_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. | IQ3_S (3.5 bpw, 11.8 GB) — the recommended quality tier | | VRAM headroom for context and KV cache | On 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. | IQ2 tiers (2.75/2.5 bpw, 9.3/8.4 GB) — the headroom tiers | | Fit on older and smaller cards | Needs 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. | IQ2 tiers (2.75/2.5 bpw, 9.3/8.4 GB) — the headroom tiers | | Interactive and batch throughput | Same 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. | IQ2 tiers (2.75/2.5 bpw, 9.3/8.4 GB) — the headroom tiers | | Setup and workflow | Plain GGUF for llama.cpp, Ollama, LM Studio; optional -mtp build (+0.35 GB) adds speculative decoding with identical weights. | Same files, same tools, same 0.9 GB mmproj for vision — zero migration cost either way. | IQ3_S (3.5 bpw, 11.8 GB) — the recommended quality tier | ## Key Statistics - **Qwen3.8-27B GGUF tiers ship at 8.4-11.8 GB; IQ3_S keeps 99.8% of the BF16 task average at a 4.6x reduction** — [ISTA-DASLab model card](https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF) (2026) - **~40 tok/s decode average and 552 tok/s prefill at 112k context on a 16-GB desktop card** — [Community RTX 5060 Ti measurements](https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF/discussions/6) (2026) ## 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. ## 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. ## Frequently Asked Questions **Q: Which GGUF file do I take on a 16-GB card?** A: 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. **Q: Is 40 tok/s on the 5060 Ti a fixed value?** A: 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. **Q: Do I need the -mtp variant?** A: 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. **Q: How do these tiers beat older dynamic quantizations?** A: 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.