gemma 3 27b it needs ~19.2 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q3_K_S quantization, expect ~19 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
6.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
12.0 tok/s
TTFT
16109 ms
Safe context
4K
Memory
22.4 GB / 16.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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 2.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 14.0 tok/s | 7533 ms | 4K |
| Coding | F | Too heavy | 12.0 tok/s | 16109 ms | 4K |
| Agentic Coding | F | Too heavy | 9.1 tok/s | 30932 ms | 4K |
| Reasoning | F | Too heavy | 12.0 tok/s | 19038 ms | 4K |
| RAG | F | Too heavy | 9.1 tok/s | 38665 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for gemma 3 27b it at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~73 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 | 72.9 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 46.5 | Offloads |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 42.0 | Offloads |
| RTX 3090 24GB | 24 GB | Q4_K_M | 39.8 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 33.8 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 33.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 33.4 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 28.2 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 26.7 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.1 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 14.6 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 13.7 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 13.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 4.8 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 3.0 | 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 3 27b it (27B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 10.5 GB | Low | C51 |
Q3_K_S | 3 | 13.2 GB | Low | F0 |
NVFP4 | 4 | 15.1 GB | Medium | F0 |
Q4_K_M | 4 | 16.5 GB | Medium | F0 |
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-unsloth--gemma-3-27b-it-gguf && lms server startOpciones de mejora
RTX 4000 Ada 20GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,250 MSRP
RTX 3090 24GBOpción 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.
~$1,499 MSRP
RTX 4090 24GBMejor 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.
~$1,599 MSRP
Yes, RTX 4070 Ti Super 16GB can run gemma 3 27b it at Q3_K_S quantization (Very compromised (needs ~2.2 GB host RAM)). The recommended Q4_K_M requires 22.4 GB which exceeds available memory, but at Q3_K_S it needs only 19.2 GB. Expected decode speed: 19.3 tok/s.
gemma 3 27b it (27B parameters) requires approximately 22.4 GB at Q4_K_M quantization. On RTX 4070 Ti Super 16GB, it fits at Q3_K_S using 19.2 GB.
The recommended quantization is Q4_K_M, but on RTX 4070 Ti Super 16GB the best fitting quantization is Q3_K_S, which uses 19.2 GB.
On RTX 4070 Ti Super 16GB, gemma 3 27b it achieves approximately 19.3 tokens per second decode speed with a time-to-first-token of 10020ms using Q3_K_S quantization.
For coding workloads, gemma 3 27b it on RTX 4070 Ti Super 16GB receives a F grade with 12.0 tok/s and 4K context.
On RTX 4070 Ti Super 16GB, gemma 3 27b it can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is —, 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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