LLM Quantization Levels Compared: Q4_K_M vs Q8_0 vs FP16 [2026]

Originally published at or , PhD researcher and creator of bitsandbytes, documented: BLOOM-176B requires 8× 80GB A100 GPUs — approximately $15,000 each — to run inference at full FP32 precision. Quantization isn't a nice-to-have. It's the only reason , GPTQ, AWQ, bitsandbytes, HQQ, AQLM, FP8, MXFP4, and torchao. But for the practitioner running models through Ollama or llama.cpp on a consumer GPU or , unified memory on Apple Silicon changes the VRAM-is-the-limit intuition — big models load but throughput becomes the real constraint. A 70B model at Q4_K_M will load into a 64 GB M3 Max's unified memory, but it generates at 8-12 tokens/sec versus 40-60 tok/s for a 7B model at the same quantization.

This is the insight that changes hardware buying decisions: on limited VRAM, running a larger model at Q4_K_M often beats running a smaller model at Q8_0. A Llama 3 70B at Q4_K_M will outperform a Llama 3 8B at Q8_0 on virtually every benchmark, even though the 8B at Q8_0 has better per-token precision. Model capability scales with parameter count far more than it scales with quantization precision.

How Floating-Point Precision Works: FP32, FP16, BF16, INT8, INT4

To understand why quantization works — and where it breaks — you need to understand what you're throwing away.

showed with LLM.int8(), is that naive INT8 quantization causes catastrophic quality loss because ~0.1% of weights — outlier feature channels in attention layers — have magnitudes that blow up the quantization error. LLM.int8() solves this with mixed-precision decomposition: those outlier channels stay in FP16 while the rest goes to INT8. This is why Q8_0 in GGUF is effectively lossless while naive INT8 is not.

INT4 (Q4_K_M) uses ~4.5 bits on average (~0.5 bytes per parameter). A 7B model fits in ~4 GB. This is where the K-quant mixed-precision strategy becomes critical.

GGUF K-Quant Naming Decoded: What _S, _M, _L Actually Mean

The naming convention in GGUF quantization files confuses almost everyone. Here's the actual system:

The K-quant family was introduced by contributor ikawrakow in llama.cpp and uses importance-matrix-weighted quantization. Instead of quantizing every layer to the same bit width, K-quants assign higher precision to layers that matter more for output quality — particularly attention matrices and the first/last transformer blocks.

The suffixes indicate the aggressiveness of this mixed-precision strategy:

• 
  _S (Small): Most layers at the target bit width, minimal higher-precision layers. Smallest file size, lowest quality within the family.


• 
  _M (Medium): A balanced mix — some layers (especially attention) get bumped to 6-bit while the rest stay at 4-bit. This is the community sweet spot.


• 
  _L (Large): More layers kept at higher precision. Largest file, best quality, but diminishing returns versus _M.

This means Q4_K_M is fundamentally different from Q4_0. Q4_0 is naive 4-bit quantization with uniform precision. Q4_K_M uses a mix of 4-bit and 6-bit layers, resulting in perplexity within ~0.1-0.15 of FP16 — far better than Q4_0, which can degrade 0.5+ perplexity points according to benchmarks in ("TheBloke"), the most prolific GGUF quantization uploader on Hugging Face with over 3,800 quantized model repositories, established the community convention of labeling these variants. His model cards — which include file size, use-case recommendations, and RAM/VRAM estimates — became the de-facto reference millions of users rely on.

Quick Reference: Quantization Levels Comparison Table

This table summarizes the key numbers across quantization levels for the most common model sizes. These figures are derived from community benchmarks, llama.cpp perplexity measurements, and machine with 64+ GB unified memory, a multi-GPU setup, or the RTX 5090 with 32 GB VRAM. A 70B at Q4_K_M fits comfortably on a Mac Studio M4 Max with 128 GB unified memory, though throughput will be limited by memory bandwidth.

Per-Use-Case Recommendations: Coding vs RAG vs Chat

This is the section no other guide provides. Different tasks have different sensitivity to quantization error, and the right level depends on what you're actually doing with the model.

Coding and Structured Output: Use Q8_0

Code generation is the most precision-sensitive , this means: run a 7B coding model at Q8_0 (~7 GB) rather than a 13B at Q4_K_M (~8 GB) when the task is pure code completion. But for broader coding assistance that includes reasoning and explanation, the 13B at Q4_K_M will be stronger overall.

RAG and Long-Context Tasks: Use Q5_K_S or Q5_K_M

Quantization error compounds over long context windows. At 2K tokens, the difference between Q4_K_M and Q8_0 is negligible. At 8K+ tokens, Q4_K_M's per-token error accumulates enough to subtly shift attention patterns and degrade retrieval-augmented outputs.

If you're building -backed retrieval systems, the retrieval quality matters more than generation precision. But Q5 gives you insurance against context-length degradation without the VRAM cost of Q8.

General Chat and Creative Writing: Use Q4_K_M

Conversational tasks are the most tolerant of quantization. Creative writing, brainstorming, summarization, and general Q&A all work excellently at Q4_K_M. The 0.10-0.15 perplexity delta is imperceptible in conversational use.

This is why Ollama defaults to Q4_K_M for most model pulls — it's the right default for the majority use case. If you're using Ollama for a , started by contributor Green-Sky and maintained by , a blanket Q4 recommendation is wrong — some model families (particularly those with fewer parameters but more aggressive training) degrade faster at Q4 than others. Always check perplexity numbers for your specific model.

Throughput Benchmarks: Tokens/sec on Consumer Hardware

Throughput on consumer hardware scales near-linearly with quantization level for memory-bound inference. Lower quantization means smaller model, which means better memory bandwidth utilization.

Here are representative token generation speeds across hardware tiers:

Hardware	Q4_K_M (7B)	Q8_0 (7B)	FP16 (7B)	Q4_K_M (13B)	Q8_0 (13B)
RTX 4090 (24 GB)	50-60 tok/s	25-30 tok/s	14-18 tok/s	30-38 tok/s	16-20 tok/s
RTX 4060 Ti (16 GB)	35-45 tok/s	18-22 tok/s	10-14 tok/s	22-28 tok/s	N/A (OOM)
Apple M3 Max (64 GB)	30-40 tok/s	18-24 tok/s	10-15 tok/s	20-28 tok/s	12-16 tok/s
Apple M3 Max (128 GB)	30-40 tok/s	18-24 tok/s	10-15 tok/s	20-28 tok/s	12-16 tok/s

Notice that more unified memory on , the 5090's 32 GB VRAM opens up the Q8_0 tier for 13B models that previously required offloading, and its higher memory bandwidth pushes Q4_K_M throughput above 70 tok/s for 7B models.

VRAM Requirements by Model Size and Quantization Level

The formula is straightforward: VRAM ≈ (parameters × bytes_per_weight) + context overhead. Context overhead varies by sequence length but typically adds 500 MB to 2 GB.

A 7B parameter model needs approximately:

• 
  FP16: ~14 GB + context = ~15-16 GB total


• 
  Q8_0: ~7 GB + context = ~8-9 GB total


• 
  Q4_K_M: ~4 GB + context = ~5-6 GB total

For setup.

Quantization and Long Context: Why It Matters for RAG

Here's something most quantization guides skip: quantization error isn't static across context length. It compounds.

Each quantized weight introduces a small error. During attention computation, these errors propagate through the softmax and get amplified as the context window grows. At 2K tokens, the accumulated error is negligible. At 8K tokens, it starts to shift attention patterns. At 32K+ tokens, Q4_K_M can produce noticeably different outputs from FP16 on the same prompt.

This has practical implications for RAG at quantized precision, the retrieval-generation quality balance shifts, and Q5 becomes the minimum viable quantization for production-grade results.

IQ-Quants: The 2026 Upgrade Path From K-Quants

The IQ-quant family — IQ4_XS, IQ3_M, IQ2_M — was stabilized in llama.cpp through 2024-2025 and represents the next evolution in GGUF quantization. IQ stands for "importance-matrix quantization" (also called imatrix calibration).

The key difference: K-quants assign precision based on layer type (attention gets more bits, FFN gets fewer). IQ-quants go further by using a calibration dataset to measure which individual weight groups matter most for output quality, then allocating bits accordingly.

The result is that IQ4_XS achieves quality comparable to Q4_K_M at a smaller file size, and IQ3_M matches Q4_K_S quality at 3-bit compression. For or run ollama show llama3 --modelfile to inspect what you currently have.

For custom GGUF files (from Hugging Face or self-quantized), create a Modelfile that points to the file:

CODE Kopieren

FROM ./your-model.Q8_0.gguf

Then run ollama create mymodel -f Modelfile. This works with any GGUF quantization variant, including IQ-quants. For more on memory bandwidth is 2-3× lower than discrete GPU memory bandwidth. A model that generates at 50 tok/s on an RTX 4090 might only hit 25 tok/s on an M3 Max at the same quantization.

Mistake 5: Not checking model-specific quantization behavior. As and QLoRA training.

VRAM-starved experimentation: IQ4_XS. Better quality than Q4_K_S at smaller size.

The cascade rule: Always prefer a larger model at lower quantization over a smaller model at higher quantization — unless your task is coding, where token precision matters more than general capability.

The quantization landscape will keep shifting. The RTX 50-series brings native FP4 hardware support. IQ-quants are making 3-bit practical where it was previously unusable. And

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