Gemma 2 27B needs ~33.7 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~50 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 well
Decode
50.2 tok/s
TTFT
3858 ms
Safe context
8K
Memory
33.7 GB / 48.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 50.2 tok/s | 2104 ms | 8K |
| Coding | A | Runs well | 50.2 tok/s | 3858 ms | 8K |
| Agentic Coding | A | Tight fit | 50.2 tok/s | 5611 ms | 8K |
| Reasoning | A | Runs well | 50.2 tok/s | 4559 ms | 8K |
| RAG | A | Tight fit | 50.2 tok/s | 7014 ms | 8K |
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 6000 Ada 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | B63 |
Q3_K_S | 3 | 13.2 GB | Low | B64 |
NVFP4 | 4 | 15.1 GB | Medium | B64 |
Q4_K_M | 4 | 16.5 GB | Medium | B65 |
Q5_K_M | 5 | 19.4 GB | High | B66 |
Q6_K | 6 | 22.1 GB | High | B66 |
Q8_0Best for your GPU | 8 | 28.9 GB | Very High | B68 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Copy-paste commands to run Gemma 2 27B on your machine.
Run
ollama run gemma2:27bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 119 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 100 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 123.1 tok/s | |
| Qwen 3.5 35B A3B | 35B | S | 108.8 tok/s | |
| Qwen 3 32B | 32B | S | 43.9 tok/s |
Yes, RTX 6000 Ada 48GB can run Gemma 2 27B with a A grade (Runs well). Expected decode speed: 50.2 tok/s.
Gemma 2 27B (27B parameters) requires approximately 33.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 27B is Q4_K_M, which balances quality and memory efficiency.
On RTX 6000 Ada 48GB, Gemma 2 27B achieves approximately 50.2 tokens per second decode speed with a time-to-first-token of 3858ms using Q4_K_M quantization.
For coding workloads, Gemma 2 27B on RTX 6000 Ada 48GB receives a A grade with 50.2 tok/s and 8K context.
On RTX 6000 Ada 48GB, Gemma 2 27B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-27b-on-rtx-6000-ada-48gb" 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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