gemma 3 27b it needs ~22.5 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M 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
2.5 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1.9 GB host RAM)
Decode
9.9 tok/s
TTFT
19463 ms
Safe context
4K
Memory
22.5 GB / 20.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% 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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0.7 GB host RAM) | 11.6 tok/s | 9108 ms | 4K |
| Coding | D | Very compromised | 9.9 tok/s | 19463 ms | 4K |
| Agentic Coding | F | Too heavy | 7.5 tok/s | 37329 ms | 4K |
| Reasoning | D | Very compromised (needs ~1.9 GB host RAM) | 9.9 tok/s | 23001 ms | 4K |
| RAG | F | Too heavy | 7.5 tok/s | 46661 ms |
How gemma 3 27b it (27B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | C51 |
Q3_K_S | 3 | 13.2 GB | Low | C50 |
NVFP4Best for your GPU |
Copy-paste commands to run gemma 3 27b it on your machine.
Run
lms load hf-unsloth--gemma-3-27b-it-gguf && lms server startUpgrade options
RTX 3090 24GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 174%.
~$1,499 MSRP
RTX PRO 4000 Blackwell 24GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 246%.
~$1,599 MSRP
RTX 4090 24GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 187%.
~$1,599 MSRP
Yes, RTX 4000 Ada 20GB can run gemma 3 27b it with a D grade (Very compromised). Expected decode speed: 9.9 tok/s.
gemma 3 27b it (27B parameters) requires approximately 22.5 GB of memory with Q4_K_M quantization.
The recommended quantization for gemma 3 27b it is Q4_K_M, which balances quality and memory efficiency.
On RTX 4000 Ada 20GB, gemma 3 27b it achieves approximately 9.9 tokens per second decode speed with a time-to-first-token of 19463ms using Q4_K_M quantization.
For coding workloads, gemma 3 27b it on RTX 4000 Ada 20GB receives a D grade with 9.9 tok/s and 4K context.
On RTX 4000 Ada 20GB, gemma 3 27b it can safely use up to 4K tokens of context. 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--gemma-3-27b-it-gguf-on-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
15.1 GB |
| Medium |
| C50 |
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.