RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+1492)
B
Raises estimated decode speed by about 518%.72.9 tok/sデコード
Raises estimated decode speed by about 518%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can gemma 3 27b it run on NVIDIA L4 24GB?
YES — With Offload
C47Usable
Estimated from fit model
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.
Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 23.2 GB, 11.8 tok/s, Runs with offload
23.2 GB required24.0 GB available
97% VRAM used
Fit status
Runs with offload
Decode
11.8 tok/s
TTFT
16352 ms
Safe context
20K
Memory
23.2 GB / 24.0 GB
Memory breakdown
Weights16.5 GB
KV Cache3.2 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsgemma 3 27b it on NVIDIA L4 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 11.8 tok/s decode · 16.4s TTFT (warm) · 30 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| 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 |
Quantization options
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 |
Get started
Copy-paste commands to run gemma 3 27b it on your machine.
Run
lms load hf-maziyarpanahi--gemma-3-27b-it-gguf && lms server startアップグレードオプション
gemma 3 27b itを快適に動かすハードウェア
RTX 5090 32GBコスパ重視RTX PRO 4500 Blackwell 32GBコスパ最良32 GB VRAM (+8)896 GB/s (+596)
C
Raises estimated decode speed by about 287%.45.7 tok/sデコード
Raises estimated decode speed by about 287%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
RTX 5000 Ada 32GBNVIDIAアップグレード32 GB VRAM (+8)576 GB/s (+276)
C
Raises estimated decode speed by about 137%.28 tok/sデコード
Raises estimated decode speed by about 137%.
Adds memory headroom for longer context windows and future model growth.
〜$4,000 MSRP
Frequently asked questions
Can NVIDIA L4 24GB run gemma 3 27b it?
Yes, NVIDIA L4 24GB can run gemma 3 27b it with a C grade (Runs with offload). Expected decode speed: 11.8 tok/s.
How much VRAM does gemma 3 27b it need?
gemma 3 27b it (27B parameters) requires approximately 23.2 GB of memory with Q4_K_M quantization.
What is the best quantization for gemma 3 27b it?
The recommended quantization for gemma 3 27b it is Q4_K_M, which balances quality and memory efficiency.
What speed will gemma 3 27b it run at on NVIDIA L4 24GB?
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.
Can NVIDIA L4 24GB run gemma 3 27b it for coding?
For coding workloads, gemma 3 27b it on NVIDIA L4 24GB receives a C grade with 11.8 tok/s and 20K context.
What context window can gemma 3 27b it use on NVIDIA L4 24GB?
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.
What should I upgrade first if gemma 3 27b it feels slow on NVIDIA L4 24GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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