Qwen3.5 27B needs ~19.2 GB VRAM. RTX 5060 Ti 16GB has 16.0 GB. With Q3_K_S quantization, expect ~10 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
6.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.5 tok/s
TTFT
29915 ms
Safe context
4K
Memory
22.4 GB / 16.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 2.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 7.5 tok/s | 14056 ms | 4K |
| Coding | F | Too heavy | 6.5 tok/s | 29915 ms | 4K |
| Agentic Coding | F | Too heavy | 4.9 tok/s | 56951 ms | 4K |
| Reasoning | F | Too heavy | 6.5 tok/s | 35354 ms | 4K |
| RAG | F | Too heavy | 4.9 tok/s | 71189 ms | 4K |
How Qwen3.5 27B (27B params) fits at each quantization level on RTX 5060 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 10.5 GB | Low | C51 |
Q3_K_S | 3 | 13.2 GB | Low | F0 |
Copy-paste commands to run Qwen3.5 27B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "unsloth/Qwen3.5-27B-GGUF" \
--hf-file "Qwen3.5-27B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
RTX 4000 Ada 20GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 49%.
~$1,250 MSRP
RTX 3090 24GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,499 MSRP
RTX 4090 24GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,599 MSRP
Yes, RTX 5060 Ti 16GB can run Qwen3.5 27B at Q3_K_S quantization (Very compromised (needs ~2.2 GB host RAM)). The recommended Q4_K_M requires 22.4 GB which exceeds available memory, but at Q3_K_S it needs only 19.2 GB. Expected decode speed: 10.3 tok/s.
Qwen3.5 27B (27B parameters) requires approximately 22.4 GB at Q4_K_M quantization. On RTX 5060 Ti 16GB, it fits at Q3_K_S using 19.2 GB.
The recommended quantization is Q4_K_M, but on RTX 5060 Ti 16GB the best fitting quantization is Q3_K_S, which uses 19.2 GB.
On RTX 5060 Ti 16GB, Qwen3.5 27B achieves approximately 10.3 tokens per second decode speed with a time-to-first-token of 18796ms using Q3_K_S quantization.
For coding workloads, Qwen3.5 27B on RTX 5060 Ti 16GB receives a F grade with 6.5 tok/s and 4K context.
On RTX 5060 Ti 16GB, Qwen3.5 27B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-27b-gguf-on-rtx-5060-ti-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
15.1 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 16.5 GB | Medium | F0 |
Q5_K_M | 5 | 19.4 GB | High | F0 |
Q6_K | 6 | 22.1 GB | High | F0 |
Q8_0 | 8 | 28.9 GB | Very High | F0 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.