gemma 3 27b it needs ~28.8 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~104 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
Fit status
Runs well
Decode
104.0 tok/s
TTFT
1862 ms
Safe context
275K
Memory
28.8 GB / 80.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 104.0 tok/s | 1015 ms | 275K |
| Coding | C | Runs well | 104.0 tok/s | 1862 ms | 275K |
| Agentic Coding | C | Runs well | 104.0 tok/s | 2708 ms | 275K |
| Reasoning | C | Runs well | 104.0 tok/s | 2200 ms | 275K |
| RAG | C | Runs well | 104.0 tok/s | 3385 ms | 275K |
How gemma 3 27b it (27B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | C41 |
Q3_K_S | 3 | 13.2 GB | Low | C41 |
NVFP4 | 4 |
Copy-paste commands to run gemma 3 27b it on your machine.
Run
lms load hf-unsloth--gemma-3-27b-it-gguf && lms server startYes, NVIDIA A100 80GB can run gemma 3 27b it with a C grade (Runs well). Expected decode speed: 104.0 tok/s.
gemma 3 27b it (27B parameters) requires approximately 28.8 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 NVIDIA A100 80GB, gemma 3 27b it achieves approximately 104.0 tokens per second decode speed with a time-to-first-token of 1862ms using Q4_K_M quantization.
For coding workloads, gemma 3 27b it on NVIDIA A100 80GB receives a C grade with 104.0 tok/s and 275K context.
On NVIDIA A100 80GB, gemma 3 27b it can safely use up to 275K 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-a100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
15.1 GB |
| Medium |
| C41 |
Q4_K_M | 4 | 16.5 GB | Medium | C41 |
Q5_K_M | 5 | 19.4 GB | High | C42 |
Q6_K | 6 | 22.1 GB | High | C42 |
Q8_0 | 8 | 28.9 GB | Very High | C44 |
F16Best for your GPU | 16 | 55.4 GB | Maximum | C48 |