Qwen3.8-27B vs Gemma 4 26B A4B: the new frontier at 24GB

You have 24GB of VRAM and you want the best model that fits. Until recently, the choice was simple: grab the biggest dense model that fits. Not anymore. On one side is Qwen3.8-27B, a dense model with 27.8 billion parameters that activates all of them every token. On the other is Gemma 4 26B A4B, a mixture-of-experts model that holds 25.2 billion parameters in total but activates only 3.8 of them per token.

These are two opposite philosophies for the same goal: big-model quality on a consumer card. And the answer is not "which one is smarter" — both are, at a level that counts. The answer that matters is another: which gives you more for the hardware you have, for the way you use it, and at what speed.

The spec sheets

	Qwen3.8-27B	Gemma 4 26B A4B
Architecture	Dense	MoE (mixture-of-experts)
Total parameters	27.8B	25.2B
Active parameters per token	27.8B (all)	3.8B
Native context	262K (extensible to ~1M)	256K
Multimodality	Text + images + video	Text + images (+ audio)
Attention	Hybrid (DeltaNet + GQA) + MTP	Standard GQA
Weights at Q4_K_M	≈16.4GB	≈16.8GB
License	Apache 2.0	Apache 2.0

Two numbers stand out. Context: Qwen starts at 262K and extends with YaRN up to a million tokens, Gemma sits at 256K. And memory at Q4_K_M: nearly identical, half a gigabyte apart. In other words, at equal VRAM and almost equal weight, you are choosing between two different ways of building the same capability.

How they actually work

Qwen3.8-27B is dense: every token passes through all 27.8 billion parameters. But it is not a classic transformer. Its attention is hybrid — in each block of four layers, three use a form of linear attention (Gated DeltaNet) and one uses standard grouped-query attention (GQA). Linear attention does not keep a vector for every token it has seen: memory does not grow with context length, which is why that model reaches 262K natively and a million extended without blowing up VRAM. On top of that, it ships an MTP (multi-token prediction) trained directly into the checkpoint, which sets the stage for speculative decoding.

Gemma 4 26B A4B makes the same bet a different way. It is a mixture-of-experts: many specialists (the experts) sit in parallel, but for each token only a few are activated — 3.8 billion in total. The model "weighs" like a 25B in memory, but the work of each token costs like a 3.8B. The 256K context is handled with standard attention; the speed comes from moving few parameters per step.

The point: Qwen gets its efficiency on context (linear attention), Gemma gets its efficiency on decoding (few active parameters). They are two different levers on the same tool.

Speed: who is faster

Here the one that uses less bandwidth wins. Decode speed depends on how many parameters you must read from VRAM per token. Gemma, with 3.8B active, moves a fraction of what Qwen moves per step: on a given card, Gemma tends to respond faster, especially in word-by-word decoding. The gap is real and you feel it when you chat a lot or generate long text.

Qwen is not slow: 27.8B dense on 24GB is a model that runs at perfectly usable speed, and linear attention saves it memory on long contexts. But at equal hardware, the MoE has a structural speed advantage. If your priority is latency — instant answers, heavy use, multiple users on one card — Gemma is the faster choice.

Quality and multilingual support

On the published benchmarks, Qwen3.8-27B holds up well: it is a model built with agentic tasks and coding in mind, and it posts some of the best results in its class there. Hybrid attention lets it manage very long contexts without losing the thread. If you want a model that completes multi-step tasks or works on large documents and huge contexts, Qwen is the right profile.

Gemma 4 aims for coverage: over 140 languages, multimodal input and solid general quality at a very low inference cost. If multilingual support and the quality/speed ratio matter to you, Gemma is hard to beat.

For Italian specifically: both hold the language well. Gemma has a structural edge on the breadth of supported languages; Qwen compensates with depth on structured tasks. For everyday use in Italian, the perceived quality gap is small: what counts more is how you use them.

Requirements: Q4_K_M and your 24GB

Both at Q4_K_M sit in a similar range: Qwen ≈16.4GB, Gemma ≈16,8GB. With 24GB of VRAM you have headroom for the weights, the KV cache, and a context that counts. This is the sweet spot for 24GB — a 3090/4090, a 5090, or an Apple Silicon with equivalent unified memory.

If you have less than 24GB: Gemma is more forgiving, because its 3.8B active parameters keep it fast even when you offload part of the weights; at Q4_0 (4-bit quantization) it drops to about 14.4GB according to Google's numbers, which makes it more flexible on less generous hardware. Qwen, being dense, suffers more when you have to split weights between GPU and RAM.

When to pick one over the other

• Pick Qwen3.8-27B if you want long, structured work: agentic coding, working on large documents, contexts up to a million tokens, video and images. You want maximum capability and it does not bother you that each token costs more.
• Pick Gemma 4 26B A4B if you want maximum fluidity: fast chat, multiple users, multilingual, and a model that stays light even under aggressive quantization. The quality is there, and the memory bill is lower.
• In practice, many people end up keeping both: Gemma as the everyday workhorse, Qwen as the "heavy lifting" model when depth is needed.

In one line

Same VRAM, same quality class, two philosophies. Gemma 26B A4B goes faster and covers more languages; Qwen3.8-27B goes deeper on contexts and long tasks. There is no outright winner: there is the right model for the way you use your card.

Sources

• Qwen3.8-27B — model card on Hugging Face
• Gemma 4 — Official overview (Google AI Studio)
• Gemma 4 26B A4B — model card on Hugging Face

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