Gemma 2 9B needs ~12.6 GB but RTX 2070 Super 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
4.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
14.0 tok/s
TTFT
13811 ms
Safe context
4K
Memory
12.6 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 12.6 GB, but this setup only exposes 8.0 GB of usable VRAM.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 23.1 tok/s | 4575 ms | 4K |
| Coding | F | Too heavy | 14.0 tok/s | 13811 ms | 4K |
| Agentic Coding | F | Too heavy | 7.8 tok/s | 35918 ms | 4K |
| Reasoning | F | Too heavy | 14.0 tok/s | 16322 ms | 4K |
| RAG | F | Too heavy | 7.8 tok/s | 44898 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Gemma 2 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 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 | 126.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 125.3 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 86.6 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 80.7 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 78.2 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 71.7 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 67.3 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 63.8 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 54.3 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 45.9 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 45.7 | Heavy offload |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 42.1 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 37.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 28.7 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 10.9 | 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 9B (9B params) fits at each quantization level on RTX 2070 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B68 |
Q3_K_S | 3 | 4.4 GB | Low | B68 |
NVFP4Best for your GPU | 4 | 5.0 GB | Medium | B67 |
Q4_K_M | 4 | 5.5 GB | Medium | F0 |
Q5_K_M | 5 | 6.5 GB | High | F0 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Upgrade options
RTX 3060 12GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 105%.
~$329 MSRP
RTX 5060 Ti 16GBBudget pickMakes 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.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA 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.
~$499 MSRP
No, Gemma 2 9B requires more memory than RTX 2070 Super 8GB provides.
Gemma 2 9B (9B parameters) requires approximately 12.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.
On RTX 2070 Super 8GB, Gemma 2 9B achieves approximately 14.0 tokens per second decode speed with a time-to-first-token of 13811ms using Q4_K_M quantization.
For coding workloads, Gemma 2 9B on RTX 2070 Super 8GB receives a F grade with 14.0 tok/s and 4K context.
On RTX 2070 Super 8GB, Gemma 2 9B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-9b-on-rtx-2070-super-8gb" 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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