Can DeepSeek LLM 67B run on NVIDIA A100 80GB?

YES — Runs Great

B64Good

Estimated from fit model

DeepSeek LLM 67B needs ~55.6 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~46 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, 45.6 tok/s, Runs well

55.6 GB required80.0 GB available

70% VRAM used

Fit status

Runs well

Decode

45.6 tok/s

TTFT

4248 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 A100 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: 45.6 tok/s decode · 4.2s TTFT (warm) · 114 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	45.6 tok/s	2317 ms	4K
Coding	B	Runs well	45.6 tok/s	4248 ms	4K
Agentic Coding	B	Runs well	45.6 tok/s	6179 ms	4K
Reasoning	B	Runs well	45.6 tok/s	5020 ms	4K
RAG	B	Runs well	45.6 tok/s	7724 ms	4K

Quantization options

How DeepSeek LLM 67B (67B params) fits at each quantization level on NVIDIA A100 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	37.5 GB	Medium	B56
Q4_K_M	4	40.9 GB	Medium	B57
Q5_K_M	5	48.2 GB	High	B58
Q6_KBest for your GPU	6	54.9 GB	High	B58
Q8_0	8	71.7 GB	Very High	F0
F16	16	137.4 GB	Maximum	F0

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

Frequently asked questions

Can NVIDIA A100 80GB run DeepSeek LLM 67B?

Yes, NVIDIA A100 80GB can run DeepSeek LLM 67B with a B grade (Runs well). Expected decode speed: 45.6 tok/s.

How much VRAM does DeepSeek LLM 67B need?

DeepSeek LLM 67B (67B parameters) requires approximately 55.6 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek LLM 67B?

The recommended quantization for DeepSeek LLM 67B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek LLM 67B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, DeepSeek LLM 67B achieves approximately 45.6 tokens per second decode speed with a time-to-first-token of 4248ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run DeepSeek LLM 67B for coding?

For coding workloads, DeepSeek LLM 67B on NVIDIA A100 80GB receives a B grade with 45.6 tok/s and 4K context.

What context window can DeepSeek LLM 67B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, 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.

See all results for NVIDIA A100 80GB See all hardware for DeepSeek LLM 67B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

45.6 tok/s

2317 ms

Coding

Runs well

45.6 tok/s

4248 ms

Agentic Coding

Runs well

45.6 tok/s

6179 ms

Reasoning

Runs well

45.6 tok/s

5020 ms

RAG

Runs well

45.6 tok/s

7724 ms

Quantization options

How DeepSeek LLM 67B (67B params) fits at each quantization level on NVIDIA A100 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	37.5 GB	Medium	B56
Q4_K_M	4	40.9 GB	Medium	B57
Q5_K_M	5	48.2 GB	High	B58
Q6_KBest for your GPU	6	54.9 GB	High	B58
Q8_0	8	71.7 GB	Very High	F0
F16	16	137.4 GB	Maximum	F0

Frequently asked questions

Can NVIDIA A100 80GB run DeepSeek LLM 67B?

Yes, NVIDIA A100 80GB can run DeepSeek LLM 67B with a B grade (Runs well). Expected decode speed: 45.6 tok/s.

How much VRAM does DeepSeek LLM 67B need?

DeepSeek LLM 67B (67B parameters) requires approximately 55.6 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek LLM 67B?

The recommended quantization for DeepSeek LLM 67B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek LLM 67B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, DeepSeek LLM 67B achieves approximately 45.6 tokens per second decode speed with a time-to-first-token of 4248ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run DeepSeek LLM 67B for coding?

For coding workloads, DeepSeek LLM 67B on NVIDIA A100 80GB receives a B grade with 45.6 tok/s and 4K context.

What context window can DeepSeek LLM 67B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, 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.