96 GB VRAM (+32)1792 GB/s (+1192)
B
Raises estimated decode speed by about 221%.40.1 tok/s decode
Raises estimated decode speed by about 221%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
Can DeepSeek LLM 67B run on NVIDIA A16 64GB?
YES — Tight Fit
B57Good
○Estimated from fit model
DeepSeek LLM 67B needs ~54.0 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~12 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced
Share:
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 54.0 GB, 12.5 tok/s, Tight fit
54.0 GB required64.0 GB available
84% VRAM used
Fit status
Tight fit
Decode
12.5 tok/s
TTFT
15547 ms
Safe context
4K
Memory
54.0 GB / 64.0 GB
Memory breakdown
Weights40.9 GB
KV Cache5.8 GB
Runtime0.9 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsDeepSeek LLM 67B on NVIDIA A16 64GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 12.5 tok/s decode · 15.5s TTFT (warm) · 31 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 12.5 tok/s | 8480 ms | 4K |
| Coding | B | Tight fit | 11.5 tok/s | 16907 ms | 4K |
| Agentic Coding | B | Tight fit | 12.5 tok/s | 22613 ms | 4K |
| Reasoning | B | Tight fit | 12.5 tok/s | 18373 ms | 4K |
| RAG | B | Tight fit | 12.5 tok/s | 28267 ms | 4K |
Quantization options
How DeepSeek LLM 67B (67B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 26.1 GB | Low | B55 |
Q3_K_S | 3 | 32.8 GB | Low | B57 |
NVFP4 | 4 |
Get started
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 options
Hardware that runs DeepSeek LLM 67B well
96 GB VRAM (+32)1597 GB/s (+997)
B
Raises estimated decode speed by about 186%.35.7 tok/s decode
Raises estimated decode speed by about 186%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
96 GB VRAM (+32)4000 GB/s (+3400)
B
Raises estimated decode speed by about 590%.86.2 tok/s decode
Raises estimated decode speed by about 590%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP