gemma 3 27b it needs ~19.2 GB VRAM. RTX 5080 16GB has 16.0 GB. With Q3_K_S quantization, expect ~23 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
14.5 tok/s
TTFT
13316 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 | 16.9 tok/s | 6257 ms | 4K |
| Coding | F | Too heavy | 14.5 tok/s | 13316 ms | 4K |
| Agentic Coding | F | Too heavy | 11.1 tok/s | 25351 ms | 4K |
| Reasoning | F | Too heavy | 14.5 tok/s | 15737 ms | 4K |
| RAG | F | Too heavy | 11.1 tok/s | 31688 ms | 4K |
How gemma 3 27b it (27B params) fits at each quantization level on RTX 5080 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 |
NVFP4 | 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 |
Copy-paste commands to run gemma 3 27b it on your machine.
Run
lms load hf-maziyarpanahi--gemma-3-27b-it-gguf && lms server startUpgrade-Optionen
RTX 4000 Ada 20GBNVIDIA-UpgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
ca. $1,250 MSRP
RTX 3090 24GBGünstige WahlMakes 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.
ca. $1,499 MSRP
RTX 4090 24GBBestes Preis-Leistungs-VerhältnisMakes 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.
ca. $1,599 MSRP
Yes, RTX 5080 16GB can run gemma 3 27b it 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: 23.1 tok/s.
gemma 3 27b it (27B parameters) requires approximately 22.4 GB at Q4_K_M quantization. On RTX 5080 16GB, it fits at Q3_K_S using 19.2 GB.
The recommended quantization is Q4_K_M, but on RTX 5080 16GB the best fitting quantization is Q3_K_S, which uses 19.2 GB.
On RTX 5080 16GB, gemma 3 27b it achieves approximately 23.1 tokens per second decode speed with a time-to-first-token of 8367ms using Q3_K_S quantization.
For coding workloads, gemma 3 27b it on RTX 5080 16GB receives a F grade with 14.5 tok/s and 4K context.
On RTX 5080 16GB, gemma 3 27b it 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.
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.
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