Gemma 2 27B needs ~28.1 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q3_K_S quantization, expect ~26 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
17.9 tok/s
TTFT
10808 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) | 27.2 tok/s | 3888 ms | 6K |
| Coding | F | Too heavy | 17.9 tok/s | 10808 ms | 6K |
| Agentic Coding | F | Too heavy | 9.4 tok/s | 29977 ms | 6K |
| Reasoning | F | Too heavy | 17.9 tok/s | 12774 ms | 6K |
| RAG | F | Too heavy | 9.4 tok/s | 37471 ms | 6K |
Inference speed
Estimated decode speed (tokens/sec) for Gemma 2 27B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~58 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 58.2 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 26.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 26.6 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 26.6 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 22.4 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 21.3 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 20.9 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 17.9 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 16.8 | Offloads |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 12.6 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 11.6 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 10.6 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 7.5 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 3.6 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.3 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Gemma 2 27B (27B params) fits at each quantization level on RTX 3090 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 gemma2:27bOpciones de mejora
RTX 5090 32GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$2,499 MSRP
RTX 5000 Ada 32GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$4,000 MSRP
Yes, RTX 3090 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: 26.1 tok/s.
Gemma 2 27B (27B parameters) requires approximately 31.3 GB at Q4_K_M quantization. On RTX 3090 24GB, it fits at Q3_K_S using 28.1 GB.
The recommended quantization is Q4_K_M, but on RTX 3090 24GB the best fitting quantization is Q3_K_S, which uses 28.1 GB.
On RTX 3090 24GB, Gemma 2 27B achieves approximately 26.1 tokens per second decode speed with a time-to-first-token of 7417ms using Q3_K_S quantization.
For coding workloads, Gemma 2 27B on RTX 3090 24GB receives a F grade with 17.9 tok/s and 6K context.
On RTX 3090 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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