DeepSeek LLM 67B needs ~52.4 GB VRAM. NVIDIA L40S 48GB has 48.0 GB. With Q4_K_M quantization, expect ~10 tok/s.
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
4.4 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~3.4 GB host RAM)
Decode
6.4 tok/s
TTFT
30020 ms
Safe context
4K
Memory
52.4 GB / 48.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~1.2 GB host RAM) | 7.3 tok/s | 14525 ms | 4K |
| Coding | C | Very compromised | 10.3 tok/s | 18805 ms | 4K |
| Agentic Coding | F | Too heavy | 8.3 tok/s | 34119 ms | 4K |
| Reasoning | C | Very compromised (needs ~3.4 GB host RAM) | 6.4 tok/s | 35479 ms | 4K |
| RAG | F | Too heavy | 5.2 tok/s | 68085 ms | 4K |
How DeepSeek LLM 67B (67B params) fits at each quantization level on NVIDIA L40S 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 26.1 GB | Low | B58 |
Q3_K_S | 3 | 32.8 GB | Low | B58 |
NVFP4Best for your GPU | 4 | 37.5 GB | Medium | B58 |
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 |
Copy-paste commands to run DeepSeek LLM 67B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "deepseek-ai/deepseek-llm-67b-chat" \
--hf-file "deepseek-llm-67b-chat-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade-Optionen
NVIDIA A16 64GBGünstige WahlRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 95%.
ca. $6,500 MSRP
RTX PRO 6000 Blackwell Workstation Edition 96GBBestes Preis-Leistungs-VerhältnisRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 527%.
ca. $9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBNVIDIA-UpgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 458%.
ca. $9,999 MSRP
Yes, NVIDIA L40S 48GB can run DeepSeek LLM 67B with a C grade (Very compromised). Expected decode speed: 10.3 tok/s.
DeepSeek LLM 67B (67B parameters) requires approximately 52.4 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 L40S 48GB, DeepSeek LLM 67B achieves approximately 10.3 tokens per second decode speed with a time-to-first-token of 18805ms using Q4_K_M quantization.
For coding workloads, DeepSeek LLM 67B on NVIDIA L40S 48GB receives a C grade with 10.3 tok/s and 4K context.
On NVIDIA L40S 48GB, 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.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/deepseek-llm-67b-on-l40s-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: