gemma 3 27b it needs ~23.2 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~12 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 with offload
Decode
11.8 tok/s
TTFT
16352 ms
Safe context
20K
Memory
23.2 GB / 24.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 11.8 tok/s | 8919 ms | 20K |
| Coding | C | Runs with offload | 11.8 tok/s | 16352 ms | 20K |
| Agentic Coding | D | Very compromised (needs ~1.5 GB host RAM) | 7.3 tok/s | 38752 ms | 20K |
| Reasoning | C | Runs with offload | 11.8 tok/s | 19325 ms | 20K |
| RAG | D | Very compromised (needs ~1.5 GB host RAM) | 7.3 tok/s | 48440 ms | 20K |
How gemma 3 27b it (27B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | C50 |
Q3_K_S | 3 | 13.2 GB | Low | C50 |
NVFP4 | 4 | 15.1 GB | Medium | C50 |
Q4_K_MBest for your GPU | 4 | 16.5 GB | Medium | C50 |
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 startOpções de upgrade
RTX 5090 32GBOpção econômicaRaises estimated decode speed by about 518%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBMelhor custo-benefícioRaises estimated decode speed by about 287%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBUpgrade NVIDIARaises estimated decode speed by about 137%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Yes, NVIDIA L4 24GB can run gemma 3 27b it with a C grade (Runs with offload). Expected decode speed: 11.8 tok/s.
gemma 3 27b it (27B parameters) requires approximately 23.2 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 L4 24GB, gemma 3 27b it achieves approximately 11.8 tokens per second decode speed with a time-to-first-token of 16352ms using Q4_K_M quantization.
For coding workloads, gemma 3 27b it on NVIDIA L4 24GB receives a C grade with 11.8 tok/s and 20K context.
On NVIDIA L4 24GB, gemma 3 27b it can safely use up to 20K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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-l4-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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