Can DeepSeek LLM 67B run on NVIDIA A800 80GB?

YES — Runs Great

B64Good

○Estimated from fit model

DeepSeek LLM 67B needs ~55.6 GB VRAM. NVIDIA A800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 55.6 GB, 40.2 tok/s, Runs well

55.6 GB required80.0 GB available

70% VRAM used

Fit status

Runs well

Decode

40.2 tok/s

TTFT

4821 ms

Safe context

Memory

55.6 GB / 80.0 GB

Memory breakdown

Weights40.9 GB

KV Cache5.8 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsDeepSeek LLM 67B on NVIDIA A800 80GB
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: 40.2 tok/s decode · 4.8s TTFT (warm) · 100 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	36.9 tok/s	2860 ms	4K
Coding	B	Runs well	36.9 tok/s	5242 ms	4K
Agentic Coding	B	Runs well	36.9 tok/s	7625 ms	4K
Reasoning	B	Runs well	36.9 tok/s	6196 ms	4K
RAG	B	Runs well	36.9 tok/s	9532 ms	4K

Quantization options

How DeepSeek LLM 67B (67B params) fits at each quantization level on NVIDIA A800 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	26.1 GB	Low	C53
Q3_K_S	3	32.8 GB	Low	C55
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 99

Upgrade options

Hardware that runs DeepSeek LLM 67B well

👁 NVIDIA
NVIDIA H20 96GBBudget pick

96 GB VRAM (+16)4000 GB/s (+2065)

Raises estimated decode speed by about 114%.86.2 tok/s decode

Raises estimated decode speed by about 114%.

~$12,000 MSRP

👁 NVIDIA
NVIDIA GH200 96GBBest value