Three models, three hardware classes: similar benchmark quality, but massively different VRAM requirements and inference efficiency. What that means for choosing hardware for sovereign AI.
Matthias Allitsch-Wutte · GEKI founder
18 August 2026 · 5 min read
Modern AI models need one thing above all: VRAM. And VRAM is expensive. The bigger a model, the more GPU memory has to be provided — and the higher the demands on servers and infrastructure.
Qwen3.8 27B, DeepSeek V4 Flash and Kimi K3 show three very different approaches. Their quality is sometimes surprisingly close in benchmarks, but their hardware requirements and inference efficiency differ massively.
The decisive questions are therefore:
How much VRAM does a model need? And how many parameters actually have to compute for each token?
| Model | ARC-AGI-2 | AA Index | Total parameters | Active per token | Model weights | Hardware |
|---|---|---|---|---|---|---|
| Qwen3.8 27B | no verified score yet | 52 | 27B | ~27B | ~31 GB | 1× RTX PRO 6000 |
| DeepSeek V4 Flash | 61.4% | 52 | 284B | 13B | ~160 GB | 2× AMD Instinct MI350P |
| Kimi K3 | 60.4% | 60 | 2.8T | 104B | ~1.56 TB | 8× B300 / 8× MI355X |
The weight figures are for the formats used in practice: Qwen3.8 27B as FP8 (~31 GB), DeepSeek V4 Flash around 160 GB, Kimi K3 FP4 (~1.56 TB).
The differences in VRAM are enormous.
Qwen3.8 27B fits comfortably on a single GPU. DeepSeek V4 Flash already needs around 160 GB for the model weights. Kimi K3, at around 1.56 TB, is in an entirely different hardware class again.
On quality the gap is much smaller. Especially notable: DeepSeek V4 Flash and Kimi K3 are practically on par on ARC-AGI-2.
Qwen3.8 27B shows how much model quality is now possible with little hardware.
The FP8 model needs only around 31 GB of VRAM. A single RTX PRO 6000 with 96 GB is therefore enough.
At the same time, Qwen3.8 27B reaches an Artificial Analysis Intelligence Index of 52 — the same value as DeepSeek V4 Flash.
That makes Qwen especially interesting when hardware requirements should stay low: one GPU, low entry cost, high model quality.
For development environments, pilot projects and smaller dedicated installations that is very attractive. For a heavily used central enterprise service, however, we’d tend to go with DeepSeek.
DeepSeek V4 Flash needs significantly more VRAM than Qwen3.8 27B, but is more inference-efficient in operation.
The model has a total of 284 billion parameters and needs around 160 GB of memory for its model weights. A single RTX PRO 6000 is therefore no longer enough. Our recommendation is 2× AMD Instinct MI350P.
Why is DeepSeek still so interesting for enterprise? The answer is mixture of experts.
Of the total 284 billion parameters, only around 13 billion are activated per token. For comparison:
So two things have to be considered separately:
DeepSeek has to keep significantly more model parameters in memory. But for each new token, only a small part of them actually has to compute. That’s exactly what makes the model interesting for high utilization.
At a central enterprise AI service, many employees, applications and agents access the same infrastructure at the same time.
Then it’s not just about how cheaply the model can be loaded at all. What becomes decisive is how efficiently the existing hardware produces tokens continuously.
Here DeepSeek has a structural advantage. The GPUs keep a large 284B model in memory, but only 13B parameters are activated per token. Qwen needs much less VRAM, but as a dense model activates around 27B parameters per token.
That’s why both statements can be true at once:
The different model architectures also lead to different hardware classes.
For Qwen3.8 27B a single RTX PRO 6000 with 96 GB of VRAM is already enough. That’s a very compact entry class for sovereign AI:
Qwen impressively shows how much model quality is now possible on a single GPU. For a heavily used central enterprise AI service, however, it wouldn’t be our first choice.
For DeepSeek V4 Flash we see 2× AMD Instinct MI350P as the configuration.
The additional VRAM keeps the large 284B model in memory. At the same time, the mixture-of-experts architecture ensures that only 13B parameters are activated per token.
For us, DeepSeek V4 Flash is therefore currently the enterprise workhorse with the best ratio of quality, inference efficiency and cost. Especially for central AI platforms with many users and high utilization, this architecture plays to its strengths.
When choosing AI hardware today, two metrics are decisive.
How much VRAM does the model need?
And: how many parameters actually have to compute per token?
Qwen3.8 27B shows how much quality is possible on a single GPU today. DeepSeek V4 Flash needs more VRAM, but is more inference-efficient — exactly what makes the model especially interesting for heavily used enterprise applications. Kimi K3 delivers even more frontier quality — but with a massive jump in hardware and operating costs.
For GEKI this results in a clear tiering:
The decisive question is therefore not only how big a model is — but how much of it has to sit in VRAM, and how much of it actually computes for each token.
From a single RTX PRO 6000 to a larger multi-GPU configuration — GEKI plans, delivers and operates the right AI factory for your model, sovereign in an Austrian data center.
