Qwen3 27B Quantization Benchmark: 8-bit vs 4-bit Split 5–5 in a Dead Heat

A Counterintuitive Result

Quantization is almost always part of the conversation when deploying large language models. Most people operate on a simple assumption: 8-bit quantization preserves finer weight information, so it should be more stable and accurate than the more aggressive 4-bit compression. Yet a hands-on benchmark of the same Qwen3 27B model under both schemes — run on the MLX framework — delivers a surprisingly nuanced answer: the 8-bit and 4-bit (OQ4E) versions each won exactly 5 out of 10 accuracy tests, ending in a dead heat.

This isn't a simple question of which is better. It reveals a more complex truth behind quantization trade-offs: precision loss doesn't distribute linearly across all tasks — it "forks" in clearly distinct directions.

The comparison is straightforward: the same Qwen3 27B model, with only the quantization scheme swapped — one side is the standard 8-bit version, the other is the 4-bit OQ4E variant. The test suite covers 10 benchmarks, most with 1,000 questions each; HumanEval uses 164 problems and TruthfulQA uses 817.

Memory Footprint: 4-bit Saves Nearly 35% VRAM

The primary motivation for quantization is always resource efficiency. In these runs:

• 8-bit version: approximately 26.85 GB VRAM
• OQ4E (4-bit) version: approximately 17.55–19.72 GB VRAM

That's a gap of roughly 8–9 GB. For consumer-grade GPUs or unified-memory devices like Apple Silicon Macs, this difference often determines whether a model can run at all — not just how well it runs. The 4-bit quantization trades heavier compression for nearly 35% memory savings, and that remains its most irreplaceable advantage.

Accuracy Showdown: A Full Fork Across Knowledge, Math, and Code

The truly interesting story is in the accuracy numbers. If 8-bit dominated across the board, the conclusion would be simple. But the results show that each version's winning tasks are scattered across entirely different task types.

Knowledge Tasks: Mixed Results

The first surprise appears in the MMLU series. On MMLU, OQ4E edges ahead at 84.5% versus 8-bit's 82.8%. But on the harder MMLU Pro, the tables turn — 8-bit wins at 60.6% versus OQ4E's 59.9%.

Here's how the other knowledge and reasoning benchmarks compare:

Benchmark	8-bit	OQ4E
Winogrande	79.2%	80.5%
MathQA	43.3%	40.7%
ARC-Challenge	96.4%	96.2%
Hellaswag	94.3%	94.8%

Aside from MathQA, most gaps are within 1 percentage point — essentially a tie.

Code Generation and Truthfulness: 8-bit Holds Steadier

On the code generation benchmark HumanEval, 8-bit scores 92.7% versus OQ4E's 89.6% — a margin of more than 3 percentage points. This supports the conventional wisdom: for precision-sensitive tasks like code, the finer weight detail preserved by 8-bit quantization genuinely helps.

On TruthfulQA, 8-bit also leads slightly at 85.6% versus OQ4E's 84.8%.

GSM8K Math Reasoning: The Most Striking Reversal

The biggest surprise is GSM8K, the grade-school math reasoning task. OQ4E answers 954 questions correctly (95.4%), while 8-bit gets only 924 (92.4%). The more aggressively compressed 4-bit version outperforms by a full 3 percentage points. This result illustrates perfectly why quantization error is so hard to predict linearly across tasks — in some cases, it can even accidentally improve performance.

CMMLU Chinese Benchmark: Essentially a Tie

The margin is smallest on CMMLU, the Chinese comprehensive test: OQ4E scores 81.9% versus 8-bit's 81.4%, a difference of just 0.5 percentage points — more of a draw than a decisive win for either side.

Speed: 4-bit's Other Trump Card

Looking at all 10 benchmarks together, 8-bit wins 5 and OQ4E wins 5 on accuracy — a genuine dead heat. But on latency, OQ4E shows a commanding advantage: it finishes faster on 9 out of 10 tests, with Winogrande being the sole exception.

The most dramatic gap is again GSM8K: OQ4E completes it in 2,450.2 seconds, while 8-bit takes 5,471.2 seconds — more than twice as long. Smaller weights mean lower memory bandwidth pressure and higher compute throughput, an effect that gets amplified on tasks requiring long reasoning chains.

No Overall Champion — Only Context-Specific Fit

The Qwen3 27B quantization benchmark delivers a clear and practical conclusion: there is no simple overall winner here.

• 8-bit quantization: retains finer weight detail and is more reliable on precision-sensitive tasks like code generation and truthfulness evaluation.
• OQ4E (4-bit) quantization: uses 8–9 GB less memory, runs faster on most tasks, and even outperforms on specific tasks like math reasoning.

The two versions don't take turns leading on the same category of task — they diverge across the full spectrum from knowledge recall to math to code. This is a reminder that choosing a quantization scheme shouldn't rely on the instinct that "more bits = better." Instead, you need to weigh task type, VRAM constraints, hardware budget, and inference speed requirements together.

If your workflow is math-reasoning-heavy and you're constrained on VRAM, 4-bit OQ4E may well be the smarter choice. If code quality is critical and hardware resources are plentiful, 8-bit is still worth the investment. Ultimately, model quantization isn't just a simple "discount" — it's a trade-off that needs to be tailored to your specific use case.