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IQ3_S vs Q4_K_M: which GGUF quantization fits 27B into 16 GB

Reviewed by Michael Kerkhoff, as of

Definition
The difference is a memory plan: Q4_K_M compresses a 27B model to roughly 15 GB, filling a 16 GB card almost completely — the remaining gap to the KV cache becomes the bottleneck for context length. IQ3_S uses per-tensor search (GSQ+RCO by IST-DASLab) to reach 11.8 GB while keeping 99.8 % of the task average versus the BF16 base — a 4.6× reduction. On a 16 GB laptop, IQ3_S is the rational endpoint; Q4_K_M stays the proven default where the card offers more room.
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IQ3_S (GSQ+RCO)Q4_K_M (classic, uniform)

Detailed Comparison

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

IQ3_S (GSQ+RCO) vs Q4_K_M (classic, uniform)
FactorIQ3_S (GSQ+RCO)Q4_K_M (classic, uniform)
File size11.8 GB — 4.6× smaller than the BF16 base (53.8 GB) Winneraround 15 GB — about 3.6× smaller than the BF16 base
KV-cache headroom3–4 GB remain on 16 GB cards — contexts up to roughly 100k tokens stay practical Winnerunder 1 GB remains — long windows hit the VRAM edge early
Task quality99.8 % task average: AIME25 100.00, GPQA-Diamond 89.39, LiveCodeBench v6 85.71 — AIME and LCB exactly at base levelnear-lossless on dense models, typically 99–100 % of the FP16 average
Decode speed~40 tok/s measured on an RTX 5060 Ti 16 GB, 552 tok/s prefill at 112k context Winnersimilarly fast on larger cards; on 16 GB the smaller file streams less weight memory
Compatibilityplain GGUF file, runs out of the box in llama.cpp, Ollama and LM Studioestablished default across all GGUF tooling
Long-context stability17–30 tok/s at the end of a full 112k window — the rate falls with KV-cache growth but stays usable Winnerwindow and cache fight over the last gigabytes; OOM risk in the upper range
Auditabilitytensor allocation shipped as .rco-allocation.txt plus importance matrix (1000 × 4096 tokens) in the repo Winnerstandardised, well-documented scheme without a tensor audit file
Multimodal supportidentical mmproj file (BF16, 0.9 GB) for all IQ tiers; MTP build +0.35 GBsame mmproj file — no disadvantage, it is identical for both tiers
Total Score · 3 ties5 / 80 / 8

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

IQ3_S wins on 16 GB cards in three dimensions — file size, KV headroom, long-context stability — at practically identical quality (99.8 % task average, AIME25 and LiveCodeBench exactly at base level). Q4_K_M keeps up where the card offers more than 18 GB and the uniform scheme is the expected standard. The decision rule: measure the longest context you actually use, then pick the file that leaves 3 GB for cache and activations — with that arithmetic, IQ3_S lands on 16 GB, Q4_K_M just under.

Choose IQ3_S (GSQ+RCO) when...
  • 16 GB VRAM is the hard ceiling
  • contexts up to ~100k with real headroom are required
  • the task average must stay above 99 %
  • you want a reproducible tensor allocation with audit file
Choose Q4_K_M (classic, uniform) when...
  • more than 18 GB of VRAM is available
  • the toolchain expects the uniform, standard-documented scheme
  • existing Q4_K_M caches should not be rebuilt
  • maximum bit width per layer with standard support matters

Common questions about this comparison answered.

Frequently Asked Questions

(01)Which file do I take on a 16 GB card?
IQ3_S at 11.8 GB for full model quality and 100k–112k context. IQ2_S (9.3 GB) if you need more headroom for large batches or longer windows — it already reaches base level on AIME25. IQ2_XS is the pick for 12 GB notebooks, where 8.4 GB plus mmproj still fits with room to spare.
(02)Does that make Q4_K_M obsolete?
No. On cards with more than 16 GB, Q4_K_M remains the proven near-lossless default and every toolchain understands it. The advantage of IQ3_S is a geometric consequence, not algorithmic magic: 3.4 GB less weight, 3.4 GB more cache.
(03)Do the 40 tok/s hold for every context?
Plan a span, not a number: up to ~50 tok/s early in the window, 25–30 tok/s near the end of a full 112k context. Prefill throughput stays at 552 tok/s, running in parallel over the prompt tokens.
(04)Do I need the -mtp variant?
If your harness supports speculative decoding: yes. The MTP head costs only 0.35 GB, leaves the weights unchanged and measurably speeds up decoding. Without MTP support in the harness, the standard file is enough.

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