Gemma 2 27B needs ~28.1 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q3_K_S quantization, expect ~14 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
7.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
9.3 tok/s
TTFT
20750 ms
Safe context
6K
Memory
31.3 GB / 24.0 GB
Offload
20%
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 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~1.1 GB host RAM) | 14.1 tok/s | 7465 ms | 6K |
| Coding | F | Too heavy | 9.3 tok/s | 20750 ms | 6K |
| Agentic Coding | F | Too heavy | 4.9 tok/s | 57550 ms | 6K |
| Reasoning | F | Too heavy | 9.3 tok/s | 24523 ms | 6K |
| RAG | F | Too heavy | 4.9 tok/s | 71937 ms | 6K |
How Gemma 2 27B (27B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | B69 |
Q3_K_S | 3 | 13.2 GB | Low | B70 |
NVFP4 | 4 | 15.1 GB | Medium | B69 |
Q4_K_MBest for your GPU | 4 | 16.5 GB | Medium | B69 |
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 2 27B on your machine.
Run
ollama run gemma2:27bUpgrade-Optionen
RTX 5090 32GBGünstige WahlMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $1,999 MSRP
RTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-VerhältnisMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $2,499 MSRP
RTX 5000 Ada 32GBNVIDIA-UpgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $4,000 MSRP
Yes, RTX 4500 Ada 24GB can run Gemma 2 27B at Q3_K_S quantization (Very compromised (needs ~1.9 GB host RAM)). The recommended Q4_K_M requires 31.3 GB which exceeds available memory, but at Q3_K_S it needs only 28.1 GB. Expected decode speed: 13.6 tok/s.
Gemma 2 27B (27B parameters) requires approximately 31.3 GB at Q4_K_M quantization. On RTX 4500 Ada 24GB, it fits at Q3_K_S using 28.1 GB.
The recommended quantization is Q4_K_M, but on RTX 4500 Ada 24GB the best fitting quantization is Q3_K_S, which uses 28.1 GB.
On RTX 4500 Ada 24GB, Gemma 2 27B achieves approximately 13.6 tokens per second decode speed with a time-to-first-token of 14240ms using Q3_K_S quantization.
For coding workloads, Gemma 2 27B on RTX 4500 Ada 24GB receives a F grade with 9.3 tok/s and 6K context.
On RTX 4500 Ada 24GB, Gemma 2 27B can safely use up to 8K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.
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.
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<iframe src="https://willitrunai.com/embed/gemma-2-27b-on-rtx-4500-ada-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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