gemma 3 27b it needs ~33.3 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~157 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
157.3 tok/s
TTFT
1231 ms
Safe context
495K
Memory
33.3 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 157.3 tok/s | 672 ms | 495K |
| Coding | C | Runs well | 157.3 tok/s | 1231 ms | 495K |
| Agentic Coding | C | Runs well | 157.3 tok/s | 1791 ms | 495K |
| Reasoning | C | Runs well | 157.3 tok/s | 1455 ms | 495K |
| RAG | C | Runs well | 157.3 tok/s | 2238 ms | 495K |
How gemma 3 27b it (27B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | D38 |
Q3_K_S | 3 | 13.2 GB | Low | D39 |
NVFP4 | 4 |
Copy-paste commands to run gemma 3 27b it on your machine.
Run
lms load hf-maziyarpanahi--gemma-3-27b-it-gguf && lms server startYes, Gaudi 3 128GB can run gemma 3 27b it with a C grade (Runs well). Expected decode speed: 157.3 tok/s.
gemma 3 27b it (27B parameters) requires approximately 33.3 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 Gaudi 3 128GB, gemma 3 27b it achieves approximately 157.3 tokens per second decode speed with a time-to-first-token of 1231ms using Q4_K_M quantization.
For coding workloads, gemma 3 27b it on Gaudi 3 128GB receives a C grade with 157.3 tok/s and 495K context.
On Gaudi 3 128GB, gemma 3 27b it can safely use up to 495K 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-maziyarpanahi--gemma-3-27b-it-gguf-on-gaudi-3-128gb" 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 |
| D39 |
Q4_K_M | 4 | 16.5 GB | Medium | D39 |
Q5_K_M | 5 | 19.4 GB | High | D39 |
Q6_K | 6 | 22.1 GB | High | D39 |
Q8_0 | 8 | 28.9 GB | Very High | C40 |
F16Best for your GPU | 16 | 55.4 GB | Maximum | C45 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.