Makes 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.
〜$1,499 MSRP
Nemotron Cascade 2 30B A3B needs ~23.2 GB but RTX 3050 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
15.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.4 tok/s
TTFT
56474 ms
Safe context
4K
Memory
23.2 GB / 8.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 23.2 GB, but this setup only exposes 8.0 GB of usable VRAM.
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 | 3.4 tok/s | 30804 ms | 4K |
| Coding | F | Too heavy | 3.4 tok/s | 56474 ms | 4K |
| Agentic Coding | F | Too heavy | 3.4 tok/s | 82144 ms | 4K |
| Reasoning | F | Too heavy | 3.4 tok/s | 66742 ms | 4K |
| RAG | F | Too heavy | 3.4 tok/s | 102680 ms | 4K |
How Nemotron Cascade 2 30B A3B (30B params) fits at each quantization level on RTX 3050 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | F0 |
Q3_K_S | 3 | 14.7 GB | Low | F0 |
NVFP4 | 4 | 16.8 GB | Medium | F0 |
Q4_K_M | 4 | 18.3 GB | Medium | F0 |
Q5_K_M | 5 | 21.6 GB | High | F0 |
Q6_K | 6 | 24.6 GB | High | F0 |
Q8_0 | 8 | 32.1 GB | Very High | F0 |
F16 | 16 | 61.5 GB | Maximum | F0 |
アップグレードオプション
Makes 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.
〜$1,499 MSRP
Makes 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.
〜$1,599 MSRP
Makes 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.
〜$1,599 MSRP
No, Nemotron Cascade 2 30B A3B requires more memory than RTX 3050 8GB provides.
Nemotron Cascade 2 30B A3B (30B parameters) requires approximately 23.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Nemotron Cascade 2 30B A3B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 8GB, Nemotron Cascade 2 30B A3B achieves approximately 3.4 tokens per second decode speed with a time-to-first-token of 56474ms using Q4_K_M quantization.
For coding workloads, Nemotron Cascade 2 30B A3B on RTX 3050 8GB receives a F grade with 3.4 tok/s and 4K context.
On RTX 3050 8GB, Nemotron Cascade 2 30B A3B can safely use up to 4K tokens of context. The model's official context limit is 262K, 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/nemotron-cascade-2-30b-a3b-on-rtx-3050-8gb" 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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