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

Inference speed

DeepSeek LLM 67B inference speed — tokens per second by GPU & Mac

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.

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

DeepSeek LLM 67B inference speed — tokens per second by GPU & Mac

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

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.