DeepSeek V4 Flash needs ~161.3 GB but RTX 2080 Ti 11GB only has 11.0 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
150.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
4K
Memory
161.3 GB / 11.0 GB
Offload
90%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 161.3 GB, but this setup only exposes 11.0 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 | 2.0 tok/s | 52800 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 96800 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 114400 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 176000 ms | 4K |
How DeepSeek V4 Flash (284B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 110.8 GB | Low | F0 |
Q3_K_S | 3 | 139.2 GB | Low | F0 |
NVFP4 | 4 | 159.0 GB | Medium | F0 |
Q4_K_M | 4 | 173.2 GB | Medium | F0 |
Q5_K_M | 5 | 204.5 GB | High | F0 |
Q6_K | 6 | 232.9 GB | High | F0 |
Q8_0 | 8 | 303.9 GB | Very High | F0 |
F16 | 16 | 582.2 GB | Maximum | F0 |
アップグレードオプション
NVIDIA B200 180GBコスパ重視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.
〜$30,000 MSRP
B100 192GBコスパ最良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.
〜$35,000 MSRP
NVIDIA GB200 192GBNVIDIAアップグレード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.
〜$60,000 MSRP
No, DeepSeek V4 Flash requires more memory than RTX 2080 Ti 11GB provides.
DeepSeek V4 Flash (284B parameters) requires approximately 161.3 GB of memory with NVFP4 quantization.
The recommended quantization for DeepSeek V4 Flash is NVFP4, which balances quality and memory efficiency.
On RTX 2080 Ti 11GB, DeepSeek V4 Flash achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using NVFP4 quantization.
For coding workloads, DeepSeek V4 Flash on RTX 2080 Ti 11GB receives a F grade with 2.0 tok/s and 4K context.
On RTX 2080 Ti 11GB, DeepSeek V4 Flash can safely use up to 4K tokens of context. The model's official context limit is 1.0M, 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/deepseek-v4-flash-on-rtx-2080-ti-11gb" 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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