EDGE OSOPEN-WEIGHT LLM COMPARISON
Qwen3.6-27B vs Qwen3.6-35B-A3BSpecs, VRAM and the hardware to run them
Two small Qwen3.6 models with the same 262K context and Apache 2.0 license. The 27B is dense, so all 27B parameters work on every token. The 35B-A3B is a mixture of experts with only 3B active per token, so it generates tokens several times faster but needs a little more memory.
Parameters
27.78B vs 35B
Active per token
27.78B vs 3B
Context
262K vs 262K
Memory, 1 user
20 GB vs 24 GB
Which should you choose?
CHOOSE QWEN3.6-27B IF
You want the most capable answers per GB of memory and are fine with slower generation.
CHOOSE QWEN3.6-35B-A3B IF
You want fast replies and many concurrent users on one GPU, and can spare a few extra GB of memory.
Still deciding between Qwen3.6-27B and Qwen3.6-35B-A3B?
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Qwen3.6-27B vs Qwen3.6-35B-A3B specs
From each developer’s model card. Green marks the lighter figure where less is better for running cost.
| Specification | Qwen3.6-27B | Qwen3.6-35B-A3B |
|---|---|---|
| Developer | Qwen | Qwen |
| Total parameters | 27.78B | 35B |
| Active per token | 27.78B (dense) | 3B |
| Design | Dense | Mixture of experts |
| Experts | None (dense) | 256 experts: 8 routed + 1 shared active per token |
| Context window | 262K native, extendable to about 1M | 262K native, extendable to about 1M |
| Input and output | Text, image and video input, text output | Text, image and video input, text output |
| Layers | 64 layers | 40 layers |
| Attention | Hybrid Gated DeltaNet (linear attention) and Gated Attention layers | Hybrid linear and full attention |
| License | Apache 2.0 | Apache 2.0 |
| Released weight formats | BF16, FP8, 4-bit | BF16, FP8, NVFP4 (4-bit) |
| Weights at 4-bit / NVFP4 | About 17 GB | About 22 GB |
VRAM needed to run them
GPU memory at the lowest released precision. Each user needs their own KV cache, so memory grows with users and context length.
| GPU memory needed (4-bit / NVFP4) | Qwen3.6-27B | Qwen3.6-35B-A3B |
|---|---|---|
| 1 user, 8K context | About 20 GB | About 24 GB |
| 1 user, 128K context | About 28 GB | About 27 GB |
| 16 users, 8K context each | About 30 GB | About 28 GB |
| 64 users, 8K context each | About 63 GB | About 40 GB |
Which GPUs can run Qwen3.6-27B and Qwen3.6-35B-A3B
Smallest setup on each system, with 8K context per user. Green fits on one GPU or system, amber needs several in one server, grey needs more than one 8-GPU server.
| Hardware | Qwen3.6-27B: 1 user | Qwen3.6-27B: 16 users | Qwen3.6-35B-A3B: 1 user | Qwen3.6-35B-A3B: 16 users |
|---|---|---|---|---|
| NVIDIA DGX Spark 128 GB per system | Yes, 1 system | Yes, 1 system | Yes, 1 system | Yes, 1 system |
| NVIDIA L40S 48 GB per GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU |
| NVIDIA RTX PRO 6000 Blackwell 96 GB per GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU |
| NVIDIA H100 NVL 94 GB per GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU |
| NVIDIA H200 NVL 141 GB per GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU |
| AMD Instinct MI300X 192 GB per GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU |
| AMD Instinct MI325X 256 GB per GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU | Yes, 1 GPU |
| NVIDIA DGX B200 1.44 TB per system | Yes, 1 system | Yes, 1 system | Yes, 1 system | Yes, 1 system |
Which is faster?
On the same hardware, Qwen3.6-35B-A3B should generate tokens about 8.8× faster for each user: it reads about 1.9 GB of weights per token (3B active at NVFP4) versus 17 GB for Qwen3.6-27B. Token generation is limited mostly by memory bandwidth, so fewer active bytes means faster replies. Real throughput also depends on batching, the inference engine and the GPU.
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.
Qwen3.6-27B vs Qwen3.6-35B-A3B questions
What is the difference between Qwen3.6-27B and Qwen3.6-35B-A3B?
Two small Qwen3.6 models with the same 262K context and Apache 2.0 license. The 27B is dense, so all 27B parameters work on every token. The 35B-A3B is a mixture of experts with only 3B active per token, so it generates tokens several times faster but needs a little more memory.
Qwen3.6-27B vs Qwen3.6-35B-A3B: which is better to run on-premise?
Choose Qwen3.6-27B if you want the most capable answers per GB of memory and are fine with slower generation. Choose Qwen3.6-35B-A3B if you want fast replies and many concurrent users on one GPU, and can spare a few extra GB of memory.
Which needs less VRAM, Qwen3.6-27B or Qwen3.6-35B-A3B?
Qwen3.6-27B. For one user with 8K context it needs about 20 GB, versus about 24 GB for Qwen3.6-35B-A3B. For 16 users it needs about 30 GB versus 28 GB.
Which is faster, Qwen3.6-27B or Qwen3.6-35B-A3B?
On the same hardware, Qwen3.6-35B-A3B should generate tokens about 8.8× faster for each user: it reads about 1.9 GB of weights per token (3B active at NVFP4) versus 17 GB for Qwen3.6-27B. Token generation is limited mostly by memory bandwidth, so fewer active bytes means faster replies.
Can I run Qwen3.6-27B locally?
Yes. At 4-bit it needs about 20 GB of GPU memory for one user with 8K context, which fits on 1 NVIDIA L40S. A team of 16 needs about 30 GB: 1 NVIDIA L40S.
Can I run Qwen3.6-35B-A3B locally?
Yes. At NVFP4 it needs about 24 GB of GPU memory for one user with 8K context, which fits on 1 NVIDIA L40S. A team of 16 needs about 28 GB: 1 NVIDIA L40S.
Can I use Qwen3.6-27B and Qwen3.6-35B-A3B commercially?
Qwen3.6-27B is released under Apache 2.0. Qwen3.6-35B-A3B is released under Apache 2.0. Check each license before a commercial deployment.
How are these numbers calculated?
Memory figures come from our VRAM calculator method: weights at the lowest released precision, plus the KV cache for each user’s context, plus about 5 to 10% runtime overhead, keeping 10% of each GPU free. Where a developer does not publish the cache size we estimate it from the architecture. These are planning estimates, not benchmarks.
Related comparisons
Sources: Hugging Face: Qwen/Qwen3.6-27B; Hugging Face: Qwen/Qwen3.6-35B-A3B. Memory figures are planning estimates; real use depends on the inference engine and settings. Last checked November 12, 2026.