Qwen3.8-4B Distilled: Q4 vs Q6 vs Q8 Quantization Compared
Hands-on benchmark of Q4, Q6, and Q8 GGUF quants for the Qwen3.8-4B distilled model, testing speed, VRAM use, and reasoning depth on llama.cpp.
Which Qwen3.8-4B quant should you actually run?
For most local setups, Q4KM is the practical choice: it loads fastest, generates at the highest tokens per second, fits under 3GB, and still produces coherent, correct reasoning. Q8 is slower and heavier but reasons more deeply and occasionally finds a more original angle on hard problems. Q6 sits in between and, in one head-to-head test, actually finished fastest overall because it wrote the shortest response. The right pick depends on whether you value speed or depth more.
TL;DR
- The Qwen3.8-4B distilled model is a full-parameter distillation of Alibaba’s much larger Qwen3.8 teacher model, built by a third-party team (Emperor) rather than Alibaba itself, using roughly 45,000 curated reasoning traces.
- On a single-GPU Ubuntu box running llama.cpp, the Q4KM quant hit the fastest time to first token (3.2 seconds) and the highest generation speed (143.5 tokens/sec).
- Q6K surprised by finishing the entire benchmark run fastest in wall-clock terms (5.38 seconds total), simply because it produced a much shorter answer than the other two quants.
- Q8 took nearly 9 seconds just to produce its first token, but it also generated the deepest reasoning trace, roughly 587 words versus 311 for Q4, and found a more original framing of the test dilemma.
- All three quantization levels reached the same core decision on a values-based reasoning prompt, which suggests the model’s reasoning ability survives compression reasonably well.
- File size scales predictably with precision: Q4KM came in at 2.78GB, with Q6 and Q8 each adding roughly 600MB to 1GB of VRAM overhead on top.
- Distillation reportedly lifted MMLU scores by nearly 20% over the base 4B model, though GSM8K math performance dropped slightly, a common tradeoff at this parameter size.
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What is the Qwen3.8-4B distilled model?
Qwen3.8-4B distilled is a compressed version of Alibaba’s Qwen3.8 line, built by taking a 2.4 trillion parameter teacher model and training a much smaller 4 billion parameter student to mimic its reasoning behavior. Rather than training the small model from scratch, the team behind it (referred to as Emperor in the source demonstration, the same group that previously released a 9 billion parameter distillation of the same family) started from a 4B hybrid architecture using gated Delta net layers and fed it around 45,000 curated prompts.
For each prompt, they captured the full reasoning trace the teacher produced and trained the student to reproduce it. The result isn’t a model that independently learned to reason. It’s a model that inherited a compressed copy of how a much larger system reasons. That distinction matters for expectations: distilled models tend to transfer broad reasoning patterns and general knowledge well, but narrower skills like precise math (GSM8K-style problems) can regress slightly compared to models trained natively at that scale.
How was the quantization comparison run?
The test used llama.cpp on a single-GPU Ubuntu machine, serving three GGUF quantizations of the same distilled model: Q4KM, Q6K, and Q8. Each quant was served individually, given the same prompt, and measured on time to first token, tokens per second, total wall-clock time, and output length.
The prompt itself was deliberately built to stress reasoning rather than retrieval or coding ability. It described a “necessary evil” scenario: a pandemic threatening millions of lives, with a cure available only by destroying the last biological sample of an extinct species that might hold future medical value. There’s no factual answer to look up and no code to write, just a forced tradeoff that requires the model to commit to a position and defend it. That kind of open-ended values reasoning is where quantization-driven quality loss tends to show up first, since it depends on nuance rather than pattern-matching a known answer.
What did the benchmark numbers show?
Across the three quants, the tradeoffs were clear and fairly predictable, with one exception:
- Time to first token: Q4KM won clearly at 3.2 seconds. Q8 took about 9.2 seconds, a gap large enough to notice in interactive use.
- Tokens per second: Q4KM again led at 143.5 tokens/sec. Q8 ran roughly 25% slower.
- Total wall-clock time: Q6K unexpectedly won at 5.38 seconds, not because it was faster per token, but because it generated a much shorter response overall.
- Output length: Q4KM produced the most thorough response at 574 tokens. Q6K’s answer was the shortest of the three.
- File size: Q4KM came in at 2.78GB, small enough to run comfortably on any GPU with more than 4GB of VRAM. Q6 and Q8 stepped up modestly in both file size and VRAM footprint, with Q8 using roughly 1GB more VRAM than Q4KM.
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The surprising result is that “faster quant” and “faster overall response” aren’t the same thing. Q4 generates tokens quickest, but Q8’s slower per-token speed was partly a function of it choosing to reason more before answering, and Q6’s low total time came from writing less rather than computing faster.
Does higher precision actually improve reasoning quality?
Yes, at least in this single test case, though the improvement was qualitative more than factual. All three quantization levels reached the same underlying decision on the pandemic-versus-extinct-species dilemma: every quant chose to destroy the sample to save lives. That consistency suggests the model’s core reasoning conclusion survived compression across Q4, Q6, and Q8 without degrading into incoherence.
Where the quants diverged was in how they got there. Q4 and Q6 both leaned on fairly direct utilitarian reasoning, weighing confirmed lives saved against speculative future value. That’s a correct approach, but a predictable one. Q8 took a different route: it reframed the dilemma itself, arguing the species was already effectively extinct in the wild and that preserving a single artificial sample amounted to “preserving a ghost” rather than a real population. That reframing is a genuinely different angle, not just a more verbose version of the same argument.
So the practical read is that higher precision didn’t change the answer, but it changed the depth and originality of the argument supporting it. Whether that’s worth the extra latency and VRAM depends entirely on the use case. A chatbot answering routine queries probably doesn’t need Q8’s extra reasoning depth. A use case built around nuanced judgment calls might.
Is Q4 the right default for local deployment?
For most people running this model locally, Q4KM is the sensible default. It’s the smallest file, the fastest to first token, the fastest raw generation speed, and it still produced a complete, functional, correct answer on a genuinely hard reasoning prompt. Fitting under 3GB means it runs on nearly any modern consumer GPU without pushing VRAM limits, which matters if you’re sharing a card with other workloads or running on modest hardware.
Q8 makes sense if your application specifically rewards deeper, more original reasoning and you can tolerate roughly triple the time-to-first-token latency. Q6 is a reasonable middle ground, though its behavior in this test (shortest response, fastest total time) suggests it may trade completeness for speed rather than offering a clean balance between Q4 and Q8. None of the three quants broke the model’s core reasoning ability, which is the more important finding: quantization down to Q4 didn’t produce nonsense or logical collapse on a genuinely ambiguous prompt.
Frequently Asked Questions
What does Q4, Q6, and Q8 mean in GGUF quantization?
These labels refer to the bit precision used to compress model weights for local inference. Lower numbers (Q4) use fewer bits per weight, producing smaller files and faster inference at some cost to precision. Higher numbers (Q8) use more bits, preserving more of the original model’s numerical precision at the cost of larger file size and slower generation.
How much VRAM does Qwen3.8-4B distilled need?
- ✕a coding agent
- ✕no-code
- ✕vibe coding
- ✕a faster Cursor
The one that tells the coding agents what to build.
The Q4KM quant runs in a file size of about 2.78GB and is described as fitting comfortably on any GPU with more than 4GB of VRAM. Q6 and Q8 quants require modestly more, with Q8 using roughly 1GB more VRAM than Q4KM in the tested configuration.
Who created the Qwen3.8-4B distilled model?
The distillation was built by a third-party team (referred to as Emperor), not by Alibaba, which develops the base Qwen models. The same team previously released a 9 billion parameter distillation from the same Qwen3.8 family before pushing the technique down to 4 billion parameters.
Does quantization hurt reasoning quality?
In the tested comparison, all three quantization levels (Q4, Q6, Q8) reached the same core conclusion on a difficult values-based reasoning prompt, suggesting the underlying reasoning ability is largely preserved even at Q4. Higher precision quants showed more elaborate and original reasoning paths, but the lower quants remained coherent and functionally correct.
Why did Q6 finish faster than Q4 in total time despite Q4 having higher tokens per second?
Q6 produced a notably shorter response than Q4 in this test, so even though Q4 generated tokens faster per second, Q6’s shorter output meant less total generation work, resulting in a lower overall wall-clock time.
