DeepSeek LLM 67B needs ~48.8 GB but RTX 3500 Ada Laptop 12GB only has 12.0 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
36.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
4K
Memory
48.8 GB / 12.0 GB
Offload
80%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 48.8 GB, but this setup only exposes 12.0 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 | 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 |
Inference speed
Estimated decode speed (tokens/sec) for DeepSeek LLM 67B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~20 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? |
|---|---|---|---|---|
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 19.5 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 16.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 14.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 12.4 | Heavy offload |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 12.3 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 11.7 | Fits |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 10.3 | Heavy offload |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 9.4 | Heavy offload |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 8.3 | Heavy offload |
| RTX 5090 32GB | 32 GB | Q4_K_M | 6.2 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 5.8 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 5.0 | Heavy offload |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 4.5 | Heavy offload |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.9 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | 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 DeepSeek LLM 67B (67B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 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 | 37.5 GB | 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 |
アップグレードオプション
RTX A6000 48GBコスパ最良Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 385%.
〜$4,650 MSRP
RTX PRO 5000 Blackwell 48GBNVIDIAアップグレードMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 860%.
〜$4,999 MSRP
NVIDIA A16 64GB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.
〜$6,500 MSRP
NVIDIA H100 80GB最大の飛躍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.
〜$40,000 MSRP
No, DeepSeek LLM 67B requires more memory than RTX 3500 Ada Laptop 12GB provides.
DeepSeek LLM 67B (67B parameters) requires approximately 48.8 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 RTX 3500 Ada Laptop 12GB, DeepSeek LLM 67B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.
For coding workloads, DeepSeek LLM 67B on RTX 3500 Ada Laptop 12GB receives a F grade with 2.0 tok/s and 4K context.
On RTX 3500 Ada Laptop 12GB, 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.
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.
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