EDGE OSQWEN · 27.8B PARAMETERS
Qwen3.8-27B hardware and VRAM requirements
Alibaba’s 27B dense vision-language model, currently one of the most downloaded open-weight models on Hugging Face. Qwen3.8-27B is a dense model with all 27.8B parameters work on every token. At FP8 it needs about 32 GB of GPU memory for one user, and about 43 GB to serve a team of 16 with 8K context each.
Total parameters
27.8B
Active per token
27.8B (dense)
Context length
262K native
Released as
BF16, FP8, NVFP4 (4-bit)
Minimum VRAM
24 GB at NVFP4
For a team of 16
43 GB at FP8
VRAM needed to run Qwen3.8-27B
GPU memory by precision, number of concurrent users and context length. Each user needs their own KV cache, so memory grows with users and context.
| Precision | Weights only | 1 user, 8K | 16 users, 8K | 16 users, 32K | LoRA fine-tune |
|---|---|---|---|---|---|
| BF16 Released | 56 GB | 63 GB | 73 GB | 99 GB | 66 GB |
| FP8 Released | 28 GB | 32 GB | 43 GB | 68 GB | 37 GB |
| NVFP4 (4-bit) 20.4 GB official NVFP4 build | 21 GB | 24 GB | 35 GB | 60 GB | 29 GB |
Full fine-tuning in mixed precision needs about 451 GB: weights, gradients and optimizer states at roughly 16 bytes per parameter. LoRA trains small adapter layers on a frozen copy of the model instead, so it needs far less.
GPUs that can run Qwen3.8-27B on-premise
For a team of 16 using Qwen3.8-27B at FP8 with 8K context each (about 43 GB), smallest fit first.
NVIDIA L40S
48 GB per GPU · 0.6 GB to spare.
NVIDIA H100 NVL
94 GB per GPU · 42 GB to spare.
NVIDIA RTX PRO 6000 Blackwell
96 GB per GPU · 44 GB to spare.
NVIDIA DGX Spark
128 GB per system · 66 GB to spare.
NVIDIA H200 NVL
141 GB per GPU · 84 GB to spare.
AMD Instinct MI300X
192 GB per GPU · 130 GB to spare.
AMD Instinct MI325X
256 GB per GPU · 188 GB to spare.
NVIDIA DGX B200
1.44 TB per system · 1.25 TB to spare.
Weighing two of these? Compare AI hardware side by side, for example H100 vs H200, RTX PRO 6000 vs RTX 5090 or DGX Spark vs Mac Studio.
Running Qwen3.8-27B for your team?
We will size the GPU server for your users and data, and set up Qwen3.8-27B on it inside your own building. Free, no obligation.
Qwen3.8-27B at a glance
FROM SIZING TO RUNNING
Sizing the hardware is the easy part.
Getting several GPUs to work as one system for a whole company is where most projects slow down. That is the part Vanik Edge OS does for you.
STEP 01 · THIS TOOL
Size it
Work out the GPU memory your models need and which GPUs or servers can hold them.
STEP 02 · VANIK EDGE OS
Cluster it
Edge OS joins your GPU servers into one cluster, splits a large model across them and keeps it running if a server fails. NVIDIA and AMD servers work side by side.
STEP 03 · VANIK EDGE OS
Run it
Your team gets AI apps with single sign-on and an audit trail, managed from one console, with your data staying inside your building.
Qwen3.8-27B GPU questions
How much VRAM to run Qwen 3.8 27B?
Qwen3.8-27B needs about 32 GB of GPU memory at FP8 for one user with 8K context: 28 GB for the weights plus the KV cache and runtime overhead. Serving 16 people at once with 8K context each needs about 43 GB.
Does Qwen3.8-27B support images?
Yes. Qwen3.8-27B is a native vision-language model: it takes text, image and video input and produces text.
Is Qwen3.8-27B open source?
Yes. Qwen3.8-27B’s weights are published openly under the Apache 2.0 license, so you can download, self-host and use it commercially.
Can Qwen3.8-27B run on a single GPU?
Yes. At FP8 with one user and 8K context it needs about 32 GB, which fits on a single NVIDIA L40S or NVIDIA H100 NVL.
Can Qwen3.8-27B run on a single H100 80GB?
Yes, for light use: at NVFP4 with one user it needs about 24 GB, within the roughly 72 GB usable on an 80 GB H100.
What is the minimum VRAM for Qwen3.8-27B?
The smallest setup is about 24 GB, using NVFP4 (4-bit) for one user with 8K context. Lower precision or shorter context saves memory, but every parameter must still fit in GPU memory.
Can I fine-tune Qwen3.8-27B?
LoRA fine-tuning at FP8 needs roughly 37 GB with 4K sequences; it trains small adapter layers on a frozen copy of the model. Full fine-tuning needs about 451 GB, which calls for a multi-GPU training cluster.
How are these numbers calculated?
Weights are parameters × bytes per parameter. The KV cache uses 64.0 KB per token per user. We add about 10% of the weights plus 1 GB for runtime overhead and keep 10% of each GPU free. Figures are planning estimates; real usage depends on the inference engine and settings.
VRAM for similar models
Specifications from Hugging Face: Qwen/Qwen3.8-27B and NVIDIA NGC: Qwen3.8-27B. Memory and bandwidth are manufacturer figures; GPU counts are rounded to 1, 2, 4 or 8 per server. All figures are planning estimates. Last checked November 10, 2026.
Run Qwen3.8-27B inside your own building
Vanik Edge OS runs open-weight models on NVIDIA DGX, AMD Instinct and other GPU servers on your premises, so your data never leaves.