DeepSeek LLM 67B needs ~50.0 GB but NVIDIA A30 24GB only has 24.0 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
26.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.1 tok/s
TTFT
62410 ms
Safe context
4K
Memory
50.0 GB / 24.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 50.0 GB, but this setup only exposes 24.0 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 | 3.5 tok/s | 30017 ms | 4K |
| Coding | F | Too heavy | 3.1 tok/s | 62410 ms | 4K |
| Agentic Coding | F | Too heavy | 2.9 tok/s | 96948 ms | 4K |
| Reasoning | F | Too heavy | 3.1 tok/s | 73757 ms | 4K |
| RAG | F | Too heavy | 2.9 tok/s | 121185 ms | 4K |
How DeepSeek LLM 67B (67B params) fits at each quantization level on NVIDIA A30 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 26.1 GB | Low | F0 |
Q3_K_S | 3 | 32.8 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
RTX A6000 48GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 213%.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 519%.
~$4,999 MSRP
NVIDIA A16 64GBBudget 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.
~$6,500 MSRP
NVIDIA H100 80GBBiggest leapMakes 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.
~$40,000 MSRP
No, DeepSeek LLM 67B requires more memory than NVIDIA A30 24GB provides.
DeepSeek LLM 67B (67B parameters) requires approximately 50.0 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek LLM 67B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA A30 24GB, DeepSeek LLM 67B achieves approximately 3.1 tokens per second decode speed with a time-to-first-token of 62410ms using Q4_K_M quantization.
For coding workloads, DeepSeek LLM 67B on NVIDIA A30 24GB receives a F grade with 3.1 tok/s and 4K context.
On NVIDIA A30 24GB, DeepSeek LLM 67B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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/deepseek-llm-67b-on-a30-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 40.9 GB | Medium | F0 |
Q5_K_M | 5 | 48.2 GB | High | F0 |
Q6_K | 6 | 54.9 GB | High | F0 |
Q8_0 | 8 | 71.7 GB | Very High | F0 |
F16 | 16 | 137.4 GB | Maximum | F0 |
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.
