HomeCan I run it?Qwen3.8-27BQwen3.8-27B on an RTX 4060 Ti 16GB
No. Qwen3.8-27B at Q4_K_M needs about 20.4 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable at a ~4k context.
Can I run Qwen3.8-27B on an RTX 4060 Ti 16GB?
Qwen3.8-27B at Q4_K_M needs about 20.4 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable. That figure is 16 GB of VRAM on the spec sheet, of which 16.0 GB is usable for weights and cache, at 288 GB/s memory bandwidth. Alibaba’s denser Qwen3.8 27B, a newer mid-size chat model in the same memory class as Gemma 3 27B. These are estimates from our formula, not measured benchmarks.
- Quant that fits
- None of Q4, Q8 or FP16 needs 20.4 GB
- Memory needed
- 20.4 GB at Q4_K_M, ~4k context
- Usable VRAM
- 16.0 GB of 16 GB VRAM
- Est. tokens/sec
- n/a does not fit
Memory at each quant
| Quant | Need vs 16.0 GB | Verdict |
|---|---|---|
| Q4_K_M | 20.4 GB | Q4Won’t fit |
| Q8_0 | 34.9 GB | Q8Won’t fit |
| FP16 | 64.9 GB | FP16Won’t fit |
Estimated tokens per second
No speed estimate: Qwen3.8-27B does not fit in the 16.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.
Context headroom
Even at a short ~4k context, the weights do not fit. Extending context would only increase the gap.
| Context | KV cache (FP16) | Total at Q4_K_M | Verdict |
|---|---|---|---|
| 4k | 0.3 GB | 20.4 GB | Won’t fit |
| 8k | 0.5 GB | 20.6 GB | Won’t fit |
| 16k | 1.1 GB | 21.2 GB | Won’t fit |
| 32k | 2.1 GB | 22.2 GB | Won’t fit |
| 64k | 4.3 GB | 24.4 GB | Won’t fit |
| 128k | 8.6 GB | 28.7 GB | Won’t fit |
What to do instead
- Run gpt-oss-20b instead (15.6 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
- To hold Qwen3.8-27B at Q4, you need about 20.4 GB. The GeForce RTX 3090 has 24.0 GB usable ($1499 list price).
- CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure.
- See the best GPU for this model family and the Qwen3.8-27B model page.
Other hardware for Qwen3.8-27B
- Qwen3.8-27B on an RTX 3060 12GBWon’t fit
- Qwen3.8-27B on an RTX 3070 TiWon’t fit
- Qwen3.8-27B on an RTX 3080Won’t fit
- Qwen3.8-27B on an RTX 3090Fits
- Qwen3.8-27B on an RTX 4070 Ti SuperWon’t fit
- Qwen3.8-27B on an RTX 4090Fits
Other models on an RTX 4060 Ti 16GB
- Qwen3-32B on an RTX 4060 Ti 16GBWon’t fit
- Qwen3-30B-A3B on an RTX 4060 Ti 16GBWon’t fit
- Gemma 3 27B on an RTX 4060 Ti 16GBWon’t fit
- Gemma 4 31B on an RTX 4060 Ti 16GBWon’t fit
- gpt-oss-20b on an RTX 4060 Ti 16GBTight
- Mistral Small 24B on an RTX 4060 Ti 16GBWon’t fit
Quick answers
- Can I run Qwen3.8-27B on an RTX 4060 Ti 16GB?
- No. Qwen3.8-27B at Q4_K_M needs about 20.4 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable.
- How much VRAM does Qwen3.8-27B need?
- About 20.4 GB at Q4_K_M, 34.9 GB at Q8_0 and 64.9 GB at FP16, estimated for a ~4k context.
- How fast is Qwen3.8-27B on an RTX 4060 Ti 16GB?
- We do not estimate tokens/sec when the model does not fit. CPU offload is possible but slow.
- What context length can I use?
- The weights already do not fit, so longer context is not the limiting factor.