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 ~13 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 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

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	12.5 tok/s	15547 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	37.5 GB	Medium	B58
Q4_K_M	4	40.9 GB	Medium	B58
Q5_K_MBest for your GPU	5	48.2 GB	High	B58
Q6_K	6	54.9 GB	High	F0
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

アップグレードオプション

DeepSeek LLM 67Bを快適に動かすハードウェア

RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視

96 GB VRAM (+32)1792 GB/s (+1192)

Raises estimated decode speed by about 221%.40.1 tok/sデコード

Raises estimated decode speed by about 221%.

Adds memory headroom for longer context windows and future model growth.

〜$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBコスパ最良

96 GB VRAM (+32)1597 GB/s (+997)

Raises estimated decode speed by about 186%.35.7 tok/sデコード

Raises estimated decode speed by about 186%.

Adds memory headroom for longer context windows and future model growth.

〜$9,999 MSRP

NVIDIA H20 96GBNVIDIAアップグレード

96 GB VRAM (+32)4000 GB/s (+3400)

Raises estimated decode speed by about 590%.86.2 tok/sデコード

Raises estimated decode speed by about 590%.

Adds memory headroom for longer context windows and future model growth.

〜$12,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run DeepSeek LLM 67B?

Yes, NVIDIA A16 64GB can run DeepSeek LLM 67B with a B grade (Tight fit). Expected decode speed: 12.5 tok/s.

How much VRAM does DeepSeek LLM 67B need?

DeepSeek LLM 67B (67B parameters) requires approximately 54.0 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 A16 64GB?

On NVIDIA A16 64GB, DeepSeek LLM 67B achieves approximately 12.5 tokens per second decode speed with a time-to-first-token of 15547ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run DeepSeek LLM 67B for coding?

For coding workloads, DeepSeek LLM 67B on NVIDIA A16 64GB receives a B grade with 12.5 tok/s and 4K context.

What context window can DeepSeek LLM 67B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, 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 A16 64GB See all hardware for DeepSeek LLM 67B

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